Mastering Systematic Evidence Maps: Enhancing Chemical Risk Management in Biomedical Research

Abstract

This article provides a comprehensive overview of systematic evidence mapping (SEM) for chemical risk management, tailored for researchers, scientists, and drug development professionals. It explores the foundational concepts of SEMs, detailing their role in identifying evidence gaps and informing policy[citation:1][citation:6]. The methodological framework is presented, including PECO criteria, systematic searches, and visualization tools[citation:1][citation:2]. Common challenges are addressed with optimization strategies, such as automation and knowledge graphs[citation:1][citation:4]. The validation and comparative analysis section contrasts SEMs with systematic reviews and highlights regulatory applications[citation:2][citation:3]. The conclusion synthesizes key takeaways and suggests future directions for integrating SEMs into biomedical and clinical research to improve evidence-based decision-making.

Understanding Systematic Evidence Maps: Foundations for Chemical Risk Assessment

Defining Systematic Evidence Maps in Chemical Risk Contexts

In chemical risk management, Systematic Evidence Maps (SEMs) are defined as queryable databases of systematically gathered research evidence that characterize broad features of an available evidence base [1] [2]. They serve as a critical problem formulation tool within regulatory and research workflows, enabling the identification of knowledge clusters and gaps across extensive scientific literature [3]. Unlike systematic reviews, which synthesize evidence to answer a narrow, focused question, SEMs maintain a broader scope to better align with the wide-ranging information needs of risk assessors and policymakers [1]. Their development is driven by the exponential growth of available toxicological data and a regulatory shift toward more transparent, evidence-based decision-making processes, as seen in programs like the US EPA's Integrated Risk Information System (IRIS) and the EU's REACH initiative [1] [2]. By providing a structured, interactive inventory of research, SEMs facilitate priority setting, inform the need for future targeted systematic reviews or primary research, and enhance the resource efficiency of evidence-based toxicology [1] [4].

Table 1: Core Characteristics of Systematic Evidence Maps versus Systematic Reviews

Characteristic	Systematic Evidence Map (SEM)	Systematic Review (SR)
Primary Objective	To systematically catalog and characterize the extent, distribution, and key parameters of an evidence base [1] [2].	To synthesize findings from studies to answer a specific, narrowly focused research question, often providing a quantitative meta-analysis [1].
Scope	Broad; designed to cover a wide range of related research questions relevant to a chemical, outcome, or policy area [1] [3].	Narrow; focused on a precise question defined by specific Population, Exposure, Comparator, and Outcome (PECO) criteria [1].
Output	A searchable, interactive database or evidence inventory with descriptive summaries and visualizations of the evidence landscape [2] [4].	A narrative and/or quantitative synthesis of results, typically concluding with a graded assessment of the evidence for a specific relationship [1].
Role in Decision-Making	Problem formulation, priority-setting, and trend-spotting. Identifies where detailed synthesis or new research is most needed [1] [3].	Directly informs specific risk assessment conclusions, such as hazard identification or dose-response analysis [1].

Application Notes: Uses in Regulatory and Research Contexts

SEMs are increasingly embedded within modern chemical risk assessment frameworks. The U.S. EPA's IRIS and PPRTV programs routinely prepare SEMs as the first step in the assessment development process [3]. These maps employ broad PECO criteria to capture mammalian animal bioassays and epidemiological studies, while also tracking supplemental evidence such as in vitro studies, pharmacokinetic models, and New Approach Methodologies (NAMs) [3]. Similarly, the Agency for Toxic Substances and Disease Registry (ATSDR) uses SEMs to systematically capture and screen new literature published after a Toxicological Profile is released, maintaining a living evidence inventory [4]. A key application is exploring susceptibility and modifying factors, as demonstrated by an SEM on inorganic arsenic that mapped the literature on intrinsic and extrinsic factors influencing susceptibility to its health effects [5]. This broad mapping supports hypothesis generation and identifies sub-questions for future systematic review. Furthermore, SEMs provide a foundational resource for evidence surveillance, allowing agencies to monitor emerging trends and efficiently update assessments as new science is published, thereby addressing the challenge of keeping pace with the expanding volume of toxicological literature [1] [2].

Table 2: Exemplary Regulatory and Research Applications of Systematic Evidence Maps

Application Context	Purpose	Example
Priority Setting & Problem Formulation	To determine the volume and characteristics of available evidence for a chemical or class, guiding the planning of future risk assessments or research initiatives [1] [3].	US EPA IRIS program uses SEMs to scope available literature before undertaking a full assessment [3].
Evidence Surveillance	To maintain a current, queryable database of literature that can be periodically updated to track new publications and emerging trends [4].	ATSDR creates SEMs for substances to inventory new research post-Tox Profile release [4].
Identifying Modifying Factors	To systematically catalog evidence on sub-populations, life stages, co-exposures, or genetic factors that may alter susceptibility to a chemical [5].	An SEM on inorganic arsenic mapped studies on biomarkers, genetics, nutrition, and co-exposures as modifying factors [5].
Cataloging Alternative Methods	To track the availability and application of New Approach Methodologies (NAMs) within a chemical-specific evidence base [3].	EPA SEM templates include tracking for high-throughput, transcriptomic, and in silico studies [3].

Core Protocol for Developing a Systematic Evidence Map

The development of an SEM follows a rigorous, multi-stage protocol designed to maximize transparency, reproducibility, and utility. The following detailed methodology synthesizes established guidance and templates from leading regulatory bodies [2] [3].

Protocol Formulation and Stakeholder Engagement

Initiate the process by defining the map's objectives and scope through stakeholder engagement. Draft a detailed, publicly available protocol specifying the research question(s), often framed using broad PECO (Population, Exposure, Comparator, Outcome) criteria. For a chemical risk SEM, the population is typically humans and/or mammalian animal models, exposure is the chemical(s) of interest, comparators are unexposed or differently exposed groups, and outcomes encompass a wide range of potential health effects [3]. The protocol must pre-define the eligibility criteria for study inclusion and the categories for data coding and extraction.

Systematic Search and Screening

Execute a comprehensive, multi-database literature search (e.g., PubMed, Scopus, Embase, TOXLINE) using a pre-defined search strategy developed with a research librarian. Searches should be supplemented by reviewing reference lists of key articles and relevant reviews. All retrieved records are imported into dedicated systematic review software (e.g., DistillerSR, Rayyan, SWIFT-Review) for management. Screening is conducted in two phases by two independent reviewers to minimize bias [3]:

• Title/Abstract Screening: Records are assessed against eligibility criteria.
• Full-Text Screening: The full texts of potentially relevant records are retrieved and assessed.

A machine learning tool may be employed to prioritize records during screening. Disagreements are resolved through consensus or by a third reviewer. The screening process and results are documented in a PRISMA-style flow diagram.

Data Extraction and Coding

For studies that pass full-text screening, relevant data is extracted into structured, web-based forms. Extraction is typically performed by a single reviewer and verified by a second [3]. Data points commonly extracted include:

• Study Identification: Author, year, journal, funding source.
• Study Design: In vivo (species, strain, sex), in vitro, epidemiological (cohort, case-control), etc.
• Exposure Details: Chemical, form, route, duration, dose levels.
• Outcome Details: Health endpoint(s) assessed, measurement method.
• Key Results: Direction and significance of effect, dose-response trends, reported quantitative data.
• Modifying Factors: Any examined co-exposures, population susceptibilities [5].

Extracted text is then coded using a controlled vocabulary or ontology (e.g., CO Exposure Ontology, UBERON) to standardize terms (e.g., coding "B[a]P," "benzo(a)pyrene," and "BaP" all as the same entity) and enable querying and grouping [2].

Study Evaluation and Data Visualization

Depending on the SEM's purpose, a study evaluation may be conducted to characterize the reliability or risk of bias of included studies. This is not a full critical appraisal as in a systematic review but may involve tagging studies based on predefined design strengths or limitations [3]. The final step is the creation of interactive visualizations and a public-facing database. Data can be visualized as interactive heatmaps, evidence atlases, or network graphs showing connections between chemicals, outcomes, and models. The extracted data is made available in open-access formats for download and exploratory analysis by end-users [2] [4].

Systematic Evidence Map (SEM) Development Workflow

The Knowledge Graph Model for Advanced Evidence Mapping

A significant advancement in SEM methodology is the transition from traditional, rigid relational databases to flexible knowledge graph models [2]. Knowledge graphs store data as a network of nodes (entities like chemicals, genes, outcomes) and edges (relationships like "causes," "inhibits," "is measured by"). This schemaless, on-read structure is uniquely suited to the heterogeneous and highly interconnected nature of environmental health data [2]. For example, a single study on "Bisphenol A" affecting "estrogen receptor" activity in "rat mammary glands" linked to "proliferative lesions" creates multiple connected nodes. This model supports complex, intuitive queries that are difficult in flat databases, such as "Find all in vitro studies where any chemical from the phthalate class is shown to activate PPARγ." When combined with formal ontologies—shared, logically defined vocabularies—knowledge graphs ensure semantic consistency and enable powerful computational reasoning and data integration across different SEMs and databases, paving the way for a connected, interoperable ecosystem of evidence for chemical risk assessment [2].

Knowledge Graph Structure for Interconnected Evidence

The Scientist's Toolkit for SEM Development

Table 3: Essential Research Reagent Solutions for Systematic Evidence Mapping

Tool Category	Specific Tool/Resource	Function in SEM Development
Project Management & Screening	DistillerSR, Rayyan, SWIFT-Review, CADIMA	Web-based platforms for managing the entire SEM workflow: de-duplication, blinded screening by multiple reviewers, and export of results [5] [3].
Search Strategy Development	PubMed, TOXLINE, Scopus, Embase, Research Librarian Consultation	Bibliographic databases for comprehensive literature retrieval. A research librarian is critical for developing a sensitive, balanced search strategy.
Machine Learning & Automation	SWIFT-Review, ASReview, RobotAnalyst	Artificial intelligence tools that learn from reviewer decisions to prioritize records during screening, significantly accelerating the process [3].
Data Extraction & Coding	Custom web forms (e.g., REDCap), Microsoft Access/Excel, Ontologies (CHEBI, UBERON, CO Exposure)	Structured forms ensure consistent data extraction. Controlled vocabularies and ontologies standardize terminology for reliable querying and integration [2].
Data Storage & Modeling	Graph Databases (Neo4j, Amazon Neptune), Relational Databases (SQL)	Graph databases are ideal for implementing flexible, interconnected knowledge graph models of evidence [2].
Visualization & Reporting	Tableau, R (ggplot2, networkD3), Python (matplotlib, plotly), PRISMA	Software for generating interactive evidence atlases, heat maps, network graphs, and standardized flow diagrams for reporting [4].
Protocol & Guideline	EPA SEM Template [3], CEE Guidelines [2], PRISMA-ScR	Foundational templates and reporting standards that ensure methodological rigor, transparency, and consistency across mapping projects.

Core Concepts and Purpose in Chemical Risk Management

A Systematic Evidence Map (SEM) is a structured database and visual synthesis of a broad body of research, designed to characterize the extent, distribution, and key features of available evidence [6]. Unlike a Systematic Review (SR), which answers a specific, narrow question with a synthesized result (e.g., the effect magnitude of a specific chemical on a specific outcome), an SEM provides a high-level overview [7] [6]. Its primary outputs are interactive, queryable databases and visualizations (e.g., heat maps, bubble charts) that reveal patterns, clusters, and, most critically, gaps in the evidence base [8] [7].

Within chemical risk management, SEMs serve two core, interconnected purposes:

• Identifying Evidence Gaps: By systematically cataloging existing literature across chemicals, outcomes, and study types, SEMs make absences visible. This allows for the strategic prioritization of future primary research or targeted systematic reviews [6].
• Informing Policy and Risk Management: SEMs provide regulators and risk assessors with a rapid, comprehensive snapshot of the evidence landscape. This supports priority-setting for chemical evaluations, resource allocation for risk assessment programs (like REACH or TSCA), and the identification of emerging risks or data-poor areas requiring precautionary attention [9] [6].

The following table contrasts SEMs with other review methodologies, highlighting their unique role in the research and policy ecosystem [7] [6].

Table 1: Comparative Analysis of Evidence Synthesis Methodologies in Chemical Risk Sciences

Feature	Systematic Review (SR)	Scoping Review	Systematic Evidence Map (SEM)
Primary Focus	Answers a precise, narrow question (e.g., PECO-defined).	Explores the breadth, scope, and nature of available evidence.	Provides a broad, structured overview and visual synthesis of an evidence base.
Depth of Analysis	Deep, involving critical appraisal, data synthesis, and meta-analysis where possible.	Moderate, descriptive, and exploratory.	Balanced; detailed cataloging and categorization without critical appraisal or synthesis of results.
Core Purpose	Provide a definitive, synthesized answer to inform a specific decision.	Identify the volume and characteristics of literature, often to plan an SR.	Identify evidence gaps, trends, and clusters to inform research prioritization and policy planning.
Typical Output	Detailed narrative report with quantitative/qualitative synthesis.	Descriptive summary of evidence coverage.	Interactive database, visual maps (heatmaps, bubble charts), and gap analysis reports.
Policy Utility	Directly supports hazard identification and dose-response assessment for specific agents.	Helps define the boundaries of a future regulatory assessment.	Informs strategic agendas, chemical prioritization, and long-term research funding decisions.

The value of SEMs is particularly high in regulatory contexts burdened by large volumes of legacy and new chemicals, where they enable a resource-efficient, transparent overview of complex evidence fields [10] [6].

Protocol for Developing a Systematic Evidence Map in Chemical Risk Research

The development of an SEM is a rigorous, protocol-driven process. The following workflow details the key stages, using the aWARE project on autism spectrum disorders (ASD) and environmental exposures as an exemplary model [8].

Systematic Evidence Map (SEM) Development Workflow

Protocol Step 1: Define Scope and Develop Protocol

• Objective: Establish a transparent, pre-defined plan to minimize bias. The protocol should be registered or published [8] [6].
• Key Actions:
  • Formulate the mapping topic (e.g., "evidence on early-life exposure to industrial chemicals and neurodevelopmental outcomes").
  • Define a broad Population-Exposure-Comparator-Outcome (PECO) framework to guide searches [6].
  • Engage stakeholders (e.g., regulators, patient groups) to ensure relevance [8] [11].

Protocol Step 2: Execute Comprehensive Literature Search

• Objective: Maximize retrieval of all relevant evidence to ensure the map's comprehensiveness [8].
• Key Actions:
  • Search multiple databases (e.g., PubMed, Web of Science, Scopus) [8].
  • Use tailored search strings with chemical terms (e.g., CAS numbers, common names), outcome terms, and study design filters.
  • Supplement with grey literature searches (regulatory reports, theses) and backward/forward citation chasing [9].

Protocol Step 3: Screen and Select Studies

• Objective: Apply eligibility criteria systematically to select studies for inclusion.
• Key Actions:
  • Use dual-independent screening at title/abstract and full-text levels to reduce error.
  • Manage the process with systematic review software (e.g., DistillerSR, Rayyan) [8].
  • Document excluded studies and reasons at the full-text stage.

Protocol Step 4: Data Extraction and Categorization

• Objective: Systematically code and categorize each study's key characteristics into a structured database [8] [6].
• Key Actions:
  • Extract metadata and study details (chemical, exposure window, outcome, model system, study design).
  • Categorize studies using a controlled vocabulary. Common dimensions include:
    • Chemical Class (e.g., phthalates, perfluoroalkyl substances, metals) [9].
    • Evidence Stream (human epidemiological, in vivo mammalian, in vitro) [8] [10].
    • Outcome Domain (e.g., carcinogenicity, neurotoxicity, endocrine disruption) [8].
    • Study Design (cohort, case-control, cross-sectional for human; subchronic, chronic for animal) [8].

Protocol Step 5: Database Creation and Visualization

• Objective: Transform extracted data into an accessible, interactive format.
• Key Actions:
  • Build a relational database or structured dataset.
  • Generate visual evidence maps. A bubble plot is a standard visualization, where axes represent two key dimensions (e.g., chemical class vs. outcome), bubble size represents the volume of evidence (e.g., number of studies), and color represents a third dimension (e.g., evidence stream) [7] [6].
• Example Output (Conceptual Data): The table below simulates the type of aggregated data used to generate such visualizations.

Table 2: Aggregated Study Counts for Evidence Map Visualization (Illustrative Data)

Chemical Class	Neuro-developmental Outcomes	Endocrine Outcomes	Carcinogenicity Outcomes	Total Studies
Phthalates	45	38	12	95
Perfluoroalkyl Substances	22	31	25	78
Heavy Metals (e.g., Pb, Cd)	67	15	48	130
Bisphenols	28	52	18	98
Pesticides (Organophosphate)	58	21	30	109

Protocol Step 6: Analyze Gaps and Inform Policy

• Objective: Interpret the map to derive actionable insights for research and policy [6].
• Key Actions:
  • Identify Evidence Clusters: Areas with high density of research (e.g., lead and neurodevelopment).
  • Identify Critical Gaps: Important chemical-outcome pairs with few or no studies (e.g., newer replacement chemicals and long-term health effects).
  • Prioritize Actions: Recommend targeted systematic reviews for clustered areas, and primary research for gaps. This directly informs risk management prioritization under frameworks like TSCA or REACH [9] [6].

Methodologies for Visualizing and Prioritizing Risk

Visual tools are essential for translating the complex data from an SEM into actionable intelligence. A Risk Matrix (or Risk Assessment Matrix) is a key tool for prioritizing identified risks based on their estimated likelihood and severity [12] [13].

Risk Matrix Construction and Use A risk matrix is a grid that evaluates and plots risks based on two axes: Likelihood of Occurrence (probability of a hazard causing harm) and Severity of Impact (consequence of that harm) [12] [14]. The intersection determines the risk level, often color-coded for immediate recognition [13] [15].

Logic of a 5x5 Risk Matrix for Chemical Prioritization

• Application to Chemical Prioritization: For a chemical risk, "Likelihood" can be derived from population exposure estimates and hazard potency, while "Severity" is based on the nature of the health effect (e.g., reversible irritation vs. cancer) [10] [12]. SEM data directly feeds this by highlighting which chemicals have evidence of high-severity outcomes.
• Weight of Evidence (WoE) Integration: Modern risk frameworks use WoE approaches to integrate evidence from multiple streams (human, animal, in vitro). SEMs catalog this evidence, and the matrix can be adapted to incorporate WoE confidence levels as a factor in scoring [9].

Experimental Protocols for Filling Identified Evidence Gaps

When an SEM identifies a critical data gap (e.g., lacking toxicological data for a high-production volume chemical), targeted experimental research is required. The protocol below outlines a tiered testing strategy that aligns with Adverse Outcome Pathway (AOP) frameworks and regulatory guidelines [10] [9].

Tiered Experimental Strategy for Data-Poor Chemicals

• Tier 1: High-Throughput Screening (HTS) and In Silico Assessment
  • Objective: Rapid, cost-efficient identification of potential bioactivity and hazard.
  • Protocol: Employ ToxCast/Tox21 HTS assays to screen for activity across hundreds of molecular targets (e.g., nuclear receptor activation, stress response pathways). Use (Q)SAR models to predict physicochemical properties and basic toxicity endpoints.
  • Outcome: Prioritize chemicals for further testing based on bioactivity profiles and structural alerts.

• Tier 2: In Vitro Mechanistic Studies

  • Objective: Confirm and characterize molecular initiating events and key cellular responses.
  • Protocol:
    • Cell Models: Use relevant human cell lines (e.g., HepG2 for hepatotoxicity, SH-SY5Y for neurotoxicity).
    • Endpoint Assays: Measure cytotoxicity (ATP assay), oxidative stress (ROS detection), genotoxicity (Comet assay), and specific pathway activation (e.g., luciferase reporter assays for endocrine disruption).
    • Dose-Response: Test a range of concentrations (typically 1 nM – 100 µM) to derive benchmark concentrations (BMCs).
  • Outcome: Refine AOPs and identify sensitive endpoints for in vivo studies.

• Tier 3: Targeted In Vivo Toxicity Study

  • Objective: Provide definitive hazard data in a whole organism for critical effects.
  • Protocol:
    • Guideline Study: Design based on OECD Test Guidelines (e.g., TG 408: 90-day oral toxicity study in rodents).
    • Dosing: Apply the suspected critical effect dose from in vitro data to set in vivo dose levels.
    • Endpoint Analysis: Include clinical observations, hematology, clinical chemistry, histopathology of key organs, and specific biomarkers identified in Tier 2.
  • Outcome: Generate data for quantitative risk assessment, establishing No-Observed-Adverse-Effect Levels (NOAELs) and points of departure (PODs).

The Scientist's Toolkit: Essential Reagents and Materials

Table 3: Key Research Reagent Solutions for Toxicity Testing

Item	Function/Description	Example Use Case
ATP Assay Kit	Measures cellular adenosine triphosphate levels as a marker of cell viability and cytotoxicity.	Determining the cytotoxic concentration range in Tier 2 in vitro screening.
ROS Detection Probe	Fluorescent probes (e.g., DCFH-DA) that detect intracellular reactive oxygen species.	Assessing oxidative stress potential of a chemical in hepatocytes.
Luciferase Reporter Plasmid	Plasmid containing a response element linked to a luciferase gene.	Quantifying activation of specific nuclear receptors (e.g., ER, AR) for endocrine disruption screening.
Histopathology Fixative	Neutral buffered formalin for tissue preservation prior to staining.	Fixing liver, kidney, and other tissues for pathological examination in Tier 3 in vivo studies.
Cryopreservation Media	Solution containing DMSO and fetal bovine serum for freezing cell lines.	Preserving stocks of primary cells or specialized cell lines for repeated assays.
MS-Grade Analytical Standards	High-purity chemical standards for mass spectrometry.	Quantifying the chemical of interest in exposure media or biological matrices (e.g., plasma, tissue).

Application to Policy: Bridging Evidence Gaps to Risk Management Decisions

The ultimate test of an SEM is its utility in shaping evidence-based policy. SEMs directly support chemical risk management frameworks by providing the foundational evidence landscape [9] [6].

• Informing Regulatory Prioritization: Regulatory bodies like the US EPA under TSCA or the European Chemicals Agency (ECHA) under REACH must prioritize thousands of chemicals for evaluation. An SEM can objectively rank chemicals based on the volume and severity of associated hazard evidence and the size of exposed populations, directly feeding into prioritization algorithms [9] [6].
• Supporting Weight of Evidence (WoE) Assessments: Modern frameworks require a transparent WoE analysis [9]. An SEM provides the complete "inventory" of evidence, preventing cherry-picking and allowing regulators to clearly document the breadth and consistency of findings across evidence streams when making a hazard identification determination [10] [6].
• Guiding Innovation for Safer Chemicals: By mapping hazards associated with existing chemical classes, SEMs can inform green chemistry initiatives. They help identify problematic molecular structures or biological activities that should be avoided in the design of safer alternatives [9].

Barriers and Recommendations for Uptake: A systems-based analysis identifies barriers to using academic research in regulation, including perceived issues with reliability, transparency, and misaligned goals between academia and regulators [11]. To overcome this:

• Develop standardized reporting guidelines for academic toxicology studies to improve their regulatory utility.
• Foster collaborative platforms where regulators and researchers co-develop SEM protocols to ensure policy relevance.
• Use SEMs as a neutral evidence inventory to build consensus among diverse stakeholders (industry, academia, NGOs) by establishing a shared view of the available data [10] [11].

In conclusion, Systematic Evidence Mapping is a transformative tool for chemical risk sciences. By moving beyond single-chemical assessments to provide a systemic, visual overview of entire evidence landscapes, SEMs directly fulfill their core purposes: they make knowledge gaps actionable and provide a robust, transparent evidence base to inform and strengthen public health policy.

Historical Evolution and Key Terminology in Evidence Synthesis

1. Historical Evolution of Evidence Synthesis for Chemical Risk Management

The methodology of evidence synthesis has evolved from narrative literature reviews to a structured, systematic family of review types designed to minimize bias and meet specific research and decision-making needs. This evolution is particularly critical in chemical risk management, where regulatory decisions require transparent, reproducible, and comprehensive assessments of often complex and conflicting scientific evidence [16].

Table 1: Historical Evolution of Key Evidence Synthesis Methodologies

Decade	Key Development	Primary Driver	Impact on Chemical Risk Management
1970s-1980s	Emergence of formal Systematic Reviews (SR) and Meta-Analysis	Need for unbiased, quantitative synthesis of clinical trial data.	Established a gold standard for integrating experimental toxicology and epidemiology studies.
1990s	Development of Scoping Reviews and Rapid Reviews.	Demand for broader mapping of literature and faster answers for policymakers.	Enabled preliminary scanning of chemical classes and expedited assessments for emerging contaminants.
Early 2000s	Conceptual introduction of Evidence Maps [17].	Need to visualize research landscapes and identify evidence gaps.	Allowed researchers to catalog and categorize large bodies of environmental health literature.
2010s	Proliferation and formalization of Systematic Evidence Maps (SEMs) [16].	Advances in information science and increased volume of research.	Provided a standardized, protocol-driven tool for structuring evidence on chemical exposures and outcomes.
2020s	Integration of SEMs into regulatory frameworks (e.g., EPA TSCA) [18].	Regulatory demand for transparency and systematic approaches.	Directly informs chemical risk evaluations, prioritization, and identification of critical data gaps.

The formalization of Systematic Evidence Maps (SEMs) represents a significant recent advancement. SEMs are defined as a systematic approach to characterizing and cataloging a broad evidence base, often using visual tools to identify trends, clusters, and gaps [16]. Unlike a systematic review, which synthesizes findings to answer a specific question, an SEM organizes evidence to inform future research or review priorities [17]. This is invaluable in chemical risk management, where regulators must navigate thousands of studies across diverse endpoints (e.g., carcinogenicity, endocrine disruption, ecotoxicity).

2. Key Terminology and Methodology Comparison

Clarity in terminology is essential for selecting the appropriate synthesis method [17].

Table 2: Comparison of Evidence Synthesis Methodologies in Chemical Risk Context

Methodology	Primary Aim	Typical Output	Appraisal Required?	Example in Chemical Risk Management
Systematic Review (SR)	Answer a focused question (e.g., PECO) via synthesis.	Quantitative (meta-analysis) or qualitative summary of effects.	Mandatory (Risk of Bias).	"Does chronic exposure to Chemical X increase the risk of liver toxicity in mammals?"
Meta-Analysis	Statistically combine quantitative results from SR.	Pooled effect estimate (e.g., odds ratio, mean difference).	Mandatory.	Statistical pooling of cancer potency factors from multiple rodent bioassays.
Scoping Review	Map key concepts, evidence types, and volume in a field.	Narrative summary with thematic analysis.	Optional.	"What research exists on the environmental fate and transport of perfluorinated alkyl substances (PFAS)?"
Rapid Review	Accelerated synthesis for timely decision-making.	Concise summary of available evidence.	Streamlined/optional.	Preliminary assessment of a novel chemical's hazard potential for regulatory prioritization.
Systematic Evidence Map (SEM)	Systematically catalog and visualize evidence to identify gaps/clusters [16].	Searchable database, matrix heatmaps, interactive visualizations.	Often optional, but recommended [16].	Mapping all published in vivo and in vitro studies on the neurodevelopmental toxicity of organophosphate pesticides.

3. Detailed Protocol for a Systematic Evidence Map (SEM) in Chemical Risk Research

The following protocol adapts the standard SEM workflow [16] for chemical risk management applications.

Protocol Title: Systematic Evidence Mapping of Human Epidemiologic and In Vivo Mammalian Studies for [Chemical Class] to Inform Hazard Assessment and Research Prioritization.

3.1. Define Scope and Stakeholder Engagement

• Objective: To identify, catalog, and visualize the available evidence on [Chemical Class] linking exposure to [Health Outcome Group, e.g., endocrine-sensitive outcomes] for the purpose of guiding future targeted systematic reviews and primary research.
• Stakeholders: Engage regulatory scientists, academic researchers, and risk assessors to finalize the review question and key definitions.

3.2. Develop and Execute a Systematic Search Strategy

• Information Sources: Search multiple electronic databases (e.g., PubMed, Embase, Scopus, TOXLINE), regulatory databases (e.g., EPA's Health and Environmental Research Online), and grey literature.
• Search Syntax: Develop a search string using chemical names (CAS RN), synonyms, and controlled vocabulary (MeSH, Emtree) combined with outcome terms.
• Timeframe: From inception to present. Searches will be updated prior to final analysis.
• Documentation: Record full search strategies for all databases for reproducibility.

3.3. Screen Studies Using A Priori Eligibility Criteria

• Population: Human populations or mammalian in vivo models.
• Exposure: [Chemical Class], defined by specific structural criteria.
• Comparator: Unexposed, low-exposed, or differently exposed control groups.
• Outcome: Pre-defined [Health Outcome Group] (e.g., serum hormone levels, reproductive organ weights, tumor incidence).
• Study Design: Include observational epidemiology (cohort, case-control) and controlled experimental studies.
• Screening Tool: Use a dedicated systematic review software (e.g., DistillerSR, Rayyan, Covidence).
• Process: Conduct title/abstract and full-text screening in duplicate, with conflicts resolved by consensus.

3.4. Code and Categorize Data from Included Studies

• Coding Form: Develop a structured data extraction form in Excel or equivalent.
• Key Variables:
  • Study Identifiers: Author, year, funding source.
  • Study Design: Experimental (species, strain, exposure regimen) or observational (cohort, population).
  • Exposure Details: Specific chemical, dose/level, duration, route.
  • Outcome Details: Specific measured endpoint, direction of effect (e.g., increase, decrease, null).
  • Appraisal: For studies where effect direction is extracted, conduct a rapid risk of bias assessment (e.g., OHAT tool for experimental studies) [16].

3.5. Visualize and Synthesize the Mapped Evidence

• Evidence Matrices (Heatmaps): Create matrices plotting key dimensions (e.g., Chemical x Outcome, Study Design x Outcome). Use color coding (e.g., green for significant effect, red for no effect, gray for not tested) to represent data availability and findings [16].
• Interactive Databases: Develop a publicly accessible, searchable database of coded studies.
• Narrative Synthesis: Describe the overall distribution and characteristics of the evidence, highlighting dense clusters and critical gaps.

4. Experimental Protocol for a Cited In Vivo Toxicity Study

The following detailed methodology exemplifies the type of primary study that would be cataloged in an SEM for chemical risk assessment.

Protocol Title: Repeated-Dose 28-Day Oral Toxicity Study of a Test Chemical in Rats (Adapted from OECD TG 407).

4.1. Test System

• Animals: Young, healthy rats (e.g., Sprague-Dawley), typically 6-8 weeks old at initiation.
• Acclimatization: Minimum 5 days in standard laboratory conditions.
• Housing: Small groups of same-sex animals in solid-bottom cages with environmental enrichment.
• Randomization: Animals randomly assigned to control and treatment groups to ensure equal mean body weights.

4.2. Test Chemical Administration

• Dose Selection: Based on a prior range-finding study. At least three dose groups and a vehicle control group are used (e.g., 0, 10, 50, 250 mg/kg/day).
• Vehicle: Appropriate solvent (e.g., corn oil, methylcellulose solution, water).
• Route and Regimen: Administered daily via oral gavage for a minimum of 28 days. Dose volume is typically 5-10 mL/kg body weight.
• Blinding: Technicians conducting dosing and observations should be blinded to group assignments where possible.

4.3. In-Life Observations and Measurements

• Clinical Observations: Twice daily for mortality and signs of morbidity.
• Detailed Physical Exams: Weekly.
• Body Weight and Food Consumption: Measured and recorded at least weekly.
• Ophthalmological Examination: Pre-test and prior to termination.

4.4. Terminal Procedures and Tissue Analysis

• Hematology & Clinical Chemistry: At termination, blood is collected for analysis (e.g., hematocrit, leukocyte count, liver enzymes, creatinine).
• Necropsy: Full gross pathological examination of all animals.
• Organ Weights: Key organs (liver, kidneys, adrenals, testes, ovaries, brain, spleen) are weighed.
• Histopathology: Preserved tissues from all control and high-dose animals, and from any organs showing gross lesions, are processed, sectioned, stained (H&E), and examined microscopically.

5. Visual Workflow: Systematic Evidence Map Process

6. The Scientist's Toolkit: Research Reagent Solutions for Toxicological Studies

Table 3: Essential Research Reagents and Materials for Toxicological Assessment

Item	Function/Description	Example in Protocol
Test Chemical (High Purity)	The substance whose toxicity is being evaluated. Must be characterized for purity and stability.	The compound administered via gavage in the 28-day study.
Vehicle/Solvent	Carrier for the test chemical. Must be non-toxic at administration volumes and allow for proper dissolution/suspension.	Corn oil, methylcellulose (0.5%), or saline used in the control and dosing solutions.
Fixative (e.g., 10% Neutral Buffered Formalin)	Preserves tissue architecture for subsequent histopathological examination by cross-linking proteins.	Used to immerse and fix organs immediately after necropsy.
Hematology & Clinical Chemistry Assay Kits	Commercial reagents and calibrators for automated analyzers to measure blood parameters (e.g., CBC, ALT, AST, BUN, creatinine).	Used on terminal blood samples to assess systemic and organ-specific toxicity.
Histological Stains (e.g., Hematoxylin & Eosin - H&E)	Standard stains for microscopic evaluation. Hematoxylin stains nuclei blue; eosin stains cytoplasm and connective tissue pink.	Applied to tissue sections mounted on slides for pathological examination.
Positive Control Compound	A chemical with known, reproducible toxic effects. Used to validate the sensitivity and performance of the experimental system.	May be used in a separate satellite group to confirm assay responsiveness (not always required in every study).
Analytical Standard for Toxicokinetics	High-purity chemical standard used to calibrate instrumentation (e.g., LC-MS/MS) for quantifying the test chemical in blood or tissue.	Essential for companion studies measuring internal dose (exposure validation).

The Role of SEMs in Evidence-Based Decision Making for Environmental Health

Systematic Evidence Maps (SEMs) represent a critical methodological advancement in environmental health science, designed to address the challenges of evidence-based chemical risk management. Unlike systematic reviews, which provide focused syntheses to answer specific questions, SEMs function as comprehensive, queryable databases of research evidence [1]. They are engineered to characterize broad features of an evidence base, making them indispensable for problem formulation, priority setting, and informing the strategic direction of more resource-intensive assessments [3] [19]. Framed within a thesis on systematic evidence mapping for chemical risk management, this document outlines the core applications, detailed protocols, and essential tools for employing SEMs to enhance the transparency, efficiency, and scientific rigor of regulatory and research decisions.

Core Applications in Risk Management and Research

SEMs serve multiple strategic functions within the risk assessment and management workflow. Their primary utility lies in providing a structured overview of vast scientific literature, enabling informed decision-making at various stages. The U.S. Environmental Protection Agency’s (EPA) Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) programs now routinely employ SEMs as a foundational step in the assessment development process [3] [19]. The following table summarizes key application areas and their purposes:

Table: Core Application Areas of Systematic Evidence Maps (SEMs) in Environmental Health

Application Area	Primary Purpose	Example Use Case
Priority Setting & Problem Formulation	To identify and refine the most critical questions for risk assessment by surveying the scope and nature of available evidence. [3] [1]	Determining which chemicals or health outcomes have sufficient or deficient evidence to warrant a full systematic review or a new assessment. [19]
Evidence Surveillance & Trendspotting	To monitor the evolution of the science base, identifying emerging methods, models, or health concerns. [1]	Tracking the growth of literature on New Approach Methodologies (NAMs) for a specific chemical class. [3]
Data Gap Identification	To systematically pinpoint where significant uncertainties or lack of data exist in the toxicological profile of a substance. [19]	Mapping all available mammalian bioassay data for a chemical to reveal unstudied exposure durations or life stages.
Informing Study Evaluation	To provide context for developing appropriate criteria to assess the reliability and relevance of individual studies. [3]	Analyzing the common methodologies used in epidemiological studies on an exposure to guide risk-of-bias assessment tools.
Facilitating Read-Across & Grouping	To enable the identification of structurally or mechanistically similar chemicals with shared data, supporting collaborative assessments. [19]	Mapping toxicological endpoints across a group of phthalates to support a class-based assessment approach.

Detailed Protocol for Generating a Systematic Evidence Map

This protocol is adapted from the established methods used by the U.S. EPA IRIS and PPRTV programs [3]. It is designed to ensure rigor, transparency, and reproducibility in the mapping process.

Phase 1: Plan & Define Scope

• Objective: Develop a structured plan that defines the boundaries and objectives of the SEM.
• Protocol:
  • Formulate the Problem: Clearly articulate the risk management or research question driving the need for the map.
  • Develop PECO Criteria: Establish broad Population, Exposure, Comparator, and Outcome criteria to guide the search. The goal is inclusivity to capture all potentially relevant mammalian animal and human epidemiological studies [3].
  • Define Supplemental Content: Determine which non-PECO information will be tracked (e.g., in vitro studies, pharmacokinetic data, genotoxicity assays, NAMs) [3].
  • Develop a Review Protocol: Document all planned methods for searching, screening, and data extraction before beginning.

Phase 2: Search & Screen Evidence

• Objective: To identify and retrieve all potentially relevant evidence from the scientific literature.
• Protocol:
  • Comprehensive Search: Execute a systematic search across multiple electronic databases (e.g., PubMed, Scopus, Embase, TOXLINE) using a pre-defined search strategy.
  • Deduplicate Records: Remove duplicate citations using reference management or systematic review software.
  • Machine-Learning Assisted Screening: Utilize specialized software with machine learning capabilities to expedite the title/abstract screening process. Typically, two independent reviewers screen records, with conflicts resolved by consensus or a third reviewer [3].
  • Full-Text Review: Apply the PECO criteria at the full-text level to finalize the set of included studies.

Phase 3: Extract & Code Data

• Objective: To characterize each included study in a structured, consistent format for easy querying and visualization.
• Protocol:
  • Design Extraction Forms: Create web-based forms to capture key study characteristics. For mammalian bioassay and epidemiological studies, this includes species, study design, exposure regimen, and health systems assessed [3].
  • Code Supplemental Information: For studies not meeting PECO but captured as supplemental content, extract relevant descriptors (e.g., model system, assay type, endpoint).
  • Pilot Extraction: Calibrate the extraction process with multiple reviewers on a subset of studies to ensure consistency.

Phase 4: Visualize & Report

• Objective: To synthesize the extracted data into accessible formats that support analysis and decision-making.
• Protocol:
  • Generate Interactive Visualizations: Use software to create queryable evidence maps (e.g., heat maps, interactive tables, network graphs) that show the distribution of studies across chemicals, outcomes, and study types.
  • Produce a Structured Report: Document the methodology, present the visualizations, and provide a narrative summary of the evidence landscape, highlighting key clusters and critical gaps.
  • Share Extracted Data: Make the underlying extracted data available in open-access formats to promote reuse and harmonization across the research community [19].

Successful implementation of an SEM requires a combination of specialized software, methodological frameworks, and data resources. The following toolkit details key components for researchers and assessors.

Table: Essential Research Reagent Solutions for Systematic Evidence Mapping

Tool Category	Specific Item/Resource	Function & Purpose in SEM
Systematic Review Software	DistillerSR, Rayyan, Covidence, EPPI-Reviewer	Platforms to manage the entire SEM workflow: importing references, dual-reviewer screening, risk-of-bias assessment, and data extraction. [3]
Machine Learning Tools	SWIFT-Review, Abstractx, ASReview	Integrates with review software to prioritize references during screening, significantly accelerating the title/abstract review phase. [3]
Evidence Mapping Template	U.S. EPA IRIS/PPRTV SEM Template [3]	A standardized methodological framework and reporting template to ensure consistency, rigor, and harmonization across different mapping projects.
Data Visualization Platforms	Tableau, R (ggplot2, plotly), Python (matplotlib, seaborn)	Enables the creation of interactive, queryable visualizations (e.g., heat maps, evidence atlases) from the extracted data to reveal patterns and gaps.
Chemical & Toxicological Databases	EPA CompTox Chemicals Dashboard, NIEHS Systematic Review Data Repository	Provides curated chemical identifiers, properties, and associated study information to support accurate chemical indexing and "read-across" within an SEM. [20]
Knowledge Organization Systems	Hazard Ontology (HaOn), Effectopedia	Structured vocabularies and ontologies to standardize the coding of health outcomes and mechanisms, enabling interoperability between evidence maps. [20]

SEMs in the Decision-Making Pathway: A Conceptual Framework

SEMs are not an end product but a critical input into a larger decision-making ecosystem for chemical risk management. They provide the evidentiary foundation that informs subsequent, more targeted analyses. The following diagram illustrates the logical relationship between SEMs and other components of the evidence-based decision-making pathway, highlighting their role in prioritizing and refining questions for systematic review and primary research.

The role of SEMs is poised to expand with advancements in informatics and artificial intelligence. Future developments will likely focus on the dynamic updating of evidence maps (living SEMs), deeper integration of NAMs data, and the application of natural language processing for automated data extraction and classification [1] [20]. Within the broader thesis of systematic evidence mapping for chemical risk management, SEMs are established as the foundational tool for transforming disparate data into structured knowledge. They empower researchers and risk managers to make transparent, efficient, and defensible decisions by providing a clear-eyed view of the scientific landscape—revealing what we know, what we don't, and where to strategically look next.

Distinguishing SEMs from Scoping Reviews and Systematic Reviews

The field of chemical risk management is defined by complexity, uncertainty, and a continuously expanding evidence base encompassing toxicological, epidemiological, and exposure science literature. Navigating this vast and heterogeneous information landscape demands rigorous, transparent, and fit-for-purpose evidence synthesis methodologies [1]. Traditional narrative reviews, while useful for expert commentary, are susceptible to selection and confirmation bias, making them inadequate for high-stakes regulatory and risk management decisions [21] [22].

Within this context, three distinct but complementary systematic approaches have emerged: Systematic Reviews (SRs), Scoping Reviews (ScRs), and Systematic Evidence Maps (SEMs). Each serves a unique function in the evidence ecosystem. SRs provide definitive, synthesized answers to narrow questions; ScRs map the breadth and nature of evidence on a broader topic; and SEMs create queryable databases to characterize an entire evidence base, facilitating prioritization and trend analysis [1] [23]. For researchers and risk assessors, selecting the appropriate methodology is critical for efficiently generating reliable, decision-relevant knowledge. This guide provides a comparative analysis, detailed protocols, and practical tools to distinguish and implement these three methodologies within chemical risk management research.

Comparative Analysis: Purpose, Methodology, and Output

The choice between a Systematic Review, Scoping Review, or Systematic Evidence Map is fundamentally dictated by the research or decision-making objective. The following table outlines their core distinctions.

Table 1: Comparative Analysis of Systematic Reviews, Scoping Reviews, and Systematic Evidence Maps

Feature	Systematic Review (SR)	Scoping Review (ScR)	Systematic Evidence Map (SEM)
Primary Purpose	To answer a specific, narrow question (e.g., on efficacy/risk) by synthesizing evidence to provide a summary estimate of effect [21] [24].	To examine the extent, range, and nature of evidence on a topic; to identify key concepts/gaps; often a precursor to an SR [21] [22].	To catalog and characterize a broad evidence base systematically; to create a searchable database for evidence surveillance, prioritization, and trend analysis [1] [23].
Typical Research Question	Focused, often framed using PICO/PECO (e.g., "Does chronic exposure to Chemical X increase the risk of liver cancer in adults?").	Broad (e.g., "What is known from the literature about the health effects of Chemical Class Y?").	Broad and inventory-focused (e.g., "What toxicological and epidemiological studies exist for 100 high-production volume chemicals?").
Core Methodology	Protocol-defined search, screening, risk-of-bias assessment, data extraction, and statistical synthesis (meta-analysis) where possible [24].	Protocol-defined search, screening, and descriptive "charting" of study characteristics. Formal quality appraisal is usually not conducted [24].	Protocol-defined search, screening, and coded extraction of key study features (e.g., chemical, organism, endpoint, study type) into a database. Synthesis is limited to descriptive summaries [23].
Critical Appraisal	Mandatory. Rigorous assessment of individual study validity/risk of bias is essential [24].	Optional. May be performed but is not a defining feature [22].	Variable. May include basic study design tagging (e.g., "animal bioassay," "cohort study") to inform suitability for later SR [23].
Key Output	A synthesized summary of effects (e.g., risk ratio), often with a certainty rating (e.g., GRADE). Directly informs guidelines/decisions [21].	A narrative or thematic summary of the literature landscape, often presented with diagrams or tables mapping evidence [24].	A searchable database or interactive visualization of the evidence base, with reports highlighting evidence clusters and critical gaps [1].
Role in Risk Management	Provides the highest level of synthesized evidence for definitive hazard/risk characterization and deriving health-based guidance values [25].	Informs problem formulation, identifies where SRs are needed/feasible, and clarifies concepts for policy development [22].	Informs research and assessment prioritization, supports chemical grouping and read-across strategies, and enables evidence surveillance for regulators [1] [23].

Publication Trends: The use of all rigorous review types is growing. Data from 2022 indicated over 40,000 published SRs compared to approximately 6,000 ScRs, though the rate of growth for ScRs has been particularly notable in recent years [26].

Detailed Methodological Protocols

Protocol for Conducting a Systematic Evidence Map (SEM)

The following protocol, framed for chemical risk assessment, is adapted from best practices for SEMs [1] [23].

1. Stakeholder Engagement and Problem Formulation

• Objective: Define the scope and objectives of the SEM in direct consultation with risk managers and stakeholders.
• Action: Establish a multi-disciplinary team. Hold scoping meetings to define the chemical space (e.g., per- and polyfluoroalkyl substances (PFAS)), population/exposure context, and health outcome domains of interest. The output is a formally documented "Problem Formulation" statement.

2. Develop and Register the A Priori Protocol

• Objective: Ensure transparency, reproducibility, and minimize bias.
• Action: Draft a detailed protocol specifying: research questions; search strategy (databases, grey sources, search strings); eligibility criteria (PECO: Population, Exposure, Comparator, Outcome); data extraction categories (e.g., chemical identifier, study type, species, sex, exposure route/duration, critical effect, LOAEL/NOAEL); and plans for data management and visualization. Register the protocol on platforms like PROSPERO or Open Science Framework.

3. Comprehensive Evidence Search

• Objective: Retrieve a broad, unbiased set of relevant records.
• Action: Execute the search across multiple bibliographic databases (e.g., PubMed, Embase, Scopus, TOXLINE). Supplement with searches of regulatory agency websites, citation chasing, and consultation with experts. Document search results and dates meticulously.

4. Screening of Evidence

• Objective: Identify studies meeting the pre-defined PECO criteria.
• Action: Use systematic review software (e.g., Covidence, Rayyan, SWIFT-Review). Conduct a pilot calibration exercise. Screening is typically performed in two phases: (1) Title/Abstract screening by two independent reviewers, with conflicts resolved by a third; (2) Full-text screening using the same dual-independent process [23].

5. Data Extraction and Coding ("Charting")

• Objective: Systematically catalog key metadata from included studies into a structured database.
• Action: Develop and pilot a standardized data extraction form. Extract descriptive data (author, year, study type) and content-specific codes (e.g., chemical CASRN, hazard endpoint codes from controlled vocabularies like MeSH or custom ontologies). This creates the core "map" database [23].

6. Evidence Mapping and Visualization

• Objective: Translate the coded database into accessible formats for analysis and communication.
• Action: Generate interactive visualizations (e.g., heat maps, evidence atlases) to show the volume and distribution of evidence across dimensions such as chemical vs. endpoint, study type over time, or species used. Use tools like Tableau, R Shiny, or EPPI-Mapper.

7. Reporting and Knowledge Translation

• Objective: Communicate findings on evidence clusters and gaps to stakeholders.
• Action: Produce a final report and interactive database. Highlight areas suitable for full systematic review (dense evidence on a specific question), critical data gaps warranting primary research, and trends in the science (e.g., shifting methodological approaches).

Protocol for Quantitative Analysis in Risk Assessment: Integrating Evidence into Models

Once an SEM or SR identifies key evidence, quantitative risk assessment (QRA) translates it into estimates of population risk [25]. The following protocol outlines a probabilistic QRA for a chemical in drinking water [27].

1. Define Assessment Context and Scenarios

• Objective: Establish the risk question and counterfactual scenarios.
• Action: Define the specific chemical, exposed population, and health endpoint. Establish the baseline scenario (current exposure) and intervention scenarios (e.g., with a new treatment technology like Granular Activated Carbon (GAC) filtration) [27].

2. Exposure Assessment (Probabilistic)

• Objective: Model the distribution of exposure in the population.
• Action:
  • Source Concentration: Fit a statistical distribution (e.g., log-normal) to monitoring data for the chemical in source water, handling non-detects appropriately.
  • Fate & Transport: Use a process model (e.g., GAC breakthrough curve model) to estimate the removal efficiency and output concentration, propagating uncertainty in model parameters (e.g., isotherm coefficients) [27].
  • Intake Calculation: Combine output concentration with probabilistic distributions of ingestion rate and body weight to estimate a population distribution of daily intake.

3. Hazard Assessment (Probabilistic)

• Objective: Characterize the dose-response relationship, incorporating uncertainty.
• Action: Identify a point of departure (POD, e.g., a benchmark dose, BMD) from a key study identified via SR/SEM. Instead of applying a single uncertainty factor (UF), define probability distributions for each UF (e.g., for interspecies differences, intraspecies variability). Derive a probability distribution for the "safe" reference dose (RfD) [27].

4. Risk Characterization (Probabilistic)

• Objective: Integrate exposure and hazard distributions to quantify risk.
• Action: Perform a probabilistic Monte Carlo simulation. In each iteration, sample a value from the exposure intake distribution and a value from the RfD distribution. Calculate the hazard quotient (HQ = Intake / RfD). Repeat thousands of times to build a probability distribution of HQ. The exceedance risk is the proportion of iterations where HQ > 1 [27].

5. Sensitivity and Uncertainty Analysis

• Objective: Identify the most influential inputs and dominant uncertainties.
• Action: Use global sensitivity analysis (e.g., Sobol indices) to rank input parameters (e.g., source concentration, BMD, specific UFs) by their contribution to variance in the HQ output. This identifies critical data gaps for future research [27].

Protocol for Extracting Risk Factors from Incident Data: A Complex Network Approach

For understanding complex accident etiologies, evidence synthesis from incident reports is key. This protocol uses the Cognitive Reliability and Error Analysis Method (CREAM) and complex network theory [28].

1. Data Collection and Preparation

• Objective: Assemble a comprehensive, non-skewed dataset of historical incident reports.
• Action: Collect detailed reports of chemical safety production accidents (e.g., 481 reports from 2010-2022) [28]. Ensure the dataset is representative across time and accident types (statistical tests like Shapiro-Wilk can check for skewness).

2. Risk Factor and Causal Chain Extraction

• Objective: Deconstruct each incident into a causal sequence of factors.
• Action: Use the CREAM extended protocol [28]:
  • For an incident outcome, consult a standard "Consequence Precedent List" to identify general antecedent categories (e.g., "equipment failure," "procedure not followed").
  • For each antecedent, treat it as a new consequence and consult the list again iteratively until root causes (human, technical, organizational, environmental) are identified [28].
  • This generates "accident chains" (e.g., Inadequate Training -> Valve Left Open -> Vapor Release -> Ignition -> Explosion).

3. Network Construction (CESRN)

• Objective: Model the system of interacting risk factors.
• Action: Define a Chemical Enterprise Safety Risk Network (CESRN) [28].
  • Nodes: Each unique risk factor (e.g., "corrosion," "high temperature," "operator fatigue") and accident outcome.
  • Edges: A directed causal link from one node to another, established if the relationship appears in an extracted accident chain.
  • Edge Weight (wij): Calculated using a co-occurrence frequency formula to represent connection strength [28].

4. Network Analysis and Quantitative Prioritization

• Objective: Identify the most influential risk factors in the network.
• Action: Calculate network metrics:
  • Dynamic Risk Value: Models how "risk energy" propagates through the network from initiating factors to final outcome.
  • Node Importance: Combines traditional metrics (degree, betweenness centrality) with dynamic risk contribution. Factors with high importance are high-leverage targets for intervention [28].

Visualizing Methodological Relationships and Workflows

Conceptual Relationship Between Evidence Synthesis Methods

Methodological Workflows for SR, SEM, and QRA

Decision Logic for Selecting an Evidence Synthesis Method

The Researcher's Toolkit: Essential Solutions for Evidence Synthesis and Risk Assessment

Table 2: Key Research Reagent Solutions for Evidence Synthesis and Risk Assessment

Tool Category	Specific Tool/Resource	Primary Function in Chemical Risk Management	Key Utility
Systematic Review Software	Covidence, Rayyan, SWIFT-Review [24] [23]	Manages the screening and data extraction phases of SRs, ScRs, and SEMs.	Enables dual-independent review, conflict resolution, and efficient handling of large citation volumes. SWIFT-Review incorporates machine learning to prioritize screening [23].
Data Extraction & Coding Tools	EPPI-Reviewer, Systematic Review Data Repository (SRDR+)	Supports the creation of custom data extraction forms and the coding of study details into structured databases for SEMs and SRs.	Facilitates standardized data capture, essential for building the coded database of an SEM or preparing for meta-analysis in an SR.
Evidence Visualization Software	Tableau, R (ggplot2, Shiny), EPPI-Mapper	Generates interactive graphs, heat maps, and evidence atlases from the data extracted in an SEM or ScR.	Translates complex coded data into intuitive visualizations to communicate evidence clusters, gaps, and trends to stakeholders [1].
Probabilistic Risk Assessment Software	@Risk, Crystal Ball, R (mc2d package)	Performs Monte Carlo simulation and sensitivity analysis for quantitative chemical risk assessment (QCRA).	Propagates uncertainty in exposure and hazard parameters to produce a probabilistic risk estimate and identify the most influential input variables [27].
Network Analysis Platforms	Gephi, Cytoscape, R (igraph package)	Analyzes complex networks of risk factors (e.g., from incident data) to calculate centrality metrics and model risk propagation.	Identifies high-leverage nodes (risk factors) within an accident causation network, guiding targeted safety interventions [28].
Toxicological/ Hazard Data Resources	EPA CompTox Chemicals Dashboard, ECOTOX Knowledgebase, Hazardous Substances Data Bank	Provides curated data on chemical properties, toxicity values, and experimental results.	Serves as a critical source for verifying extracted data, identifying chemicals for inclusion, and supporting read-across within SEMs and SRs.

Step-by-Step SEM Methodology: From Protocol to Interactive Visualization

Developing a PECO Framework for Broad Evidence Capture in Risk Assessment

Within the domain of chemical risk management research, the systematic identification, characterization, and synthesis of a rapidly expanding and heterogeneous evidence base present a significant challenge. Systematic Evidence Maps (SEMs) have emerged as a critical tool for addressing this complexity, serving as problem-formulation instruments and decision-support aids for priority setting [29]. These maps provide a visual overview of the available literature, encompassing studies that meet core Population, Exposure, Comparator, Outcome (PECO) criteria as well as supplemental evidence streams [29]. This document details the development and application of a structured PECO framework designed to facilitate broad evidence capture, enabling the integration of traditional toxicological data with New Approach Methodologies (NAMs)—including in vitro, in silico, and high-throughput systems—into a cohesive risk assessment strategy [30] [31]. The protocols outlined herein are designed to ensure transparency, reproducibility, and utility for researchers and risk assessors navigating complex chemical safety questions.

PECO Framework Architecture for Broad Evidence Capture

The PECO framework provides the foundational structure for formulating precise research questions that guide systematic review and evidence mapping. A well-constructed PECO ensures the review's objectives are clear, directs the development of inclusion criteria, and aids in interpreting the directness of the evidence to the question at hand [32]. In the context of broad evidence capture for chemical risk assessment, each component must be carefully considered to encompass traditional and novel data streams.

Core PECO Components & Elaboration Guidance:

• Population (P): Defines the subjects of interest. For human health risk assessment, this includes human populations (which may be further specified by life stage, susceptibility, or occupational status) and experimental animal models. A broad capture approach also considers the biological systems used in NAMs (e.g., specific human cell lines, engineered tissues, or non-mammalian models like zebrafish embryos) as relevant populations for mechanistic evidence [30].
• Exposure (E): Describes the chemical, mixture, or stressor under investigation. Specifications include the agent's identity, form, duration, frequency, route, and magnitude of exposure. For NAMs and in vitro studies, this translates to dosing regimens, concentrations, and exposure media [32].
• Comparator (C): Defines the reference scenario against which the exposure is evaluated. In human epidemiology, this is typically an unexposed or low-exposed group. In controlled animal or in vitro studies, it is the concurrent control group (e.g., vehicle control). The comparator is critical for defining the effect measure (e.g., risk ratio, mean difference) [32].
• Outcome (O): Specifies the health effects, endpoints, or biomarkers measured. This ranges from apical outcomes like mortality, clinical signs, or disease incidence in whole organisms to key events, pathway perturbations, and cellular biomarkers in mechanistic and NAM studies [29].

Framing PECO for Different Assessment Phases: The formulation of the PECO statement must align with the specific research or decision-making context [32]. The U.S. EPA IRIS program typically keeps PECO criteria broad to identify all mammalian bioassay and epidemiological studies informative for hazard identification [29]. The framework can be applied across five paradigmatic scenarios [32]:

Table: PECO Framework Application Scenarios for Risk Assessment

Scenario & Context	Primary Objective	Example PECO Question
1. Exploratory Hazard Identification	To determine if any association exists between exposure and outcome.	In mammalian animal models (P), does oral exposure to Chemical X (E), compared to no exposure (C), affect liver weight or histopathology (O)?
2. Characterizing Dose-Response	To explore the shape and distribution of the exposure-outcome relationship.	In a human cohort (P), what is the change in biomarker Y (O) per 10 µg/L increase in serum concentration of Chemical X (E)?
3. Evaluating a Defined Exposure Level	To assess the effect of a specific exposure cut-off (e.g., a regulatory limit).	In occupational workers (P), what is the risk of respiratory symptom Z (O) when exposed to air concentrations ≥ 1 ppm (E), compared to exposures < 0.1 ppm (C)?
4. Integrating Mechanistic Evidence	To incorporate data from NAMs to support biological plausibility.	In human hepatocyte spheroids (P), does treatment with Chemical X (E), compared to vehicle control (C), alter the expression of genes in pathway A (O)?
5. Assessing an Intervention	To evaluate the effect of an exposure reduction.	In a community (P), does the implementation of filtration (intervention reducing E), compared to no intervention, lower the incidence of health effect B (O)?

Systematic Evidence Mapping (SEM) Protocols

The SEM process translates the PECO framework into a actionable, transparent methodology for evidence inventory and characterization. The following protocol, adapted from templates used by the U.S. EPA's IRIS and PPRTV programs, provides a step-by-step workflow [29].

Protocol Workflow

Diagram: Systematic Evidence Mapping Protocol Workflow

Detailed Methodology

Step 1: Problem Formulation & PECO Definition Define the assessment's scope and objectives. Develop and document the primary PECO statement(s). Clearly distinguish between core evidence (studies directly meeting PECO for hazard identification) and supplemental evidence (mechanistic, toxicokinetic, NAMs, or exposure-only studies) to be tracked [29].

Step 2: Search Strategy & Execution Develop a comprehensive search string using controlled vocabulary (e.g., MeSH) and keywords for PECO elements. Search multiple databases (e.g., PubMed, Web of Science, Scopus) without date or language restrictions [8]. The search should be documented for full reproducibility.

Step 3-4: Screening & Eligibility Assessment Utilize systematic review software (e.g., DistillerSR, Rayyan) to manage the process. Screening occurs in two phases: 1) Title/Abstract, and 2) Full-Text. At least two independent reviewers assess studies against the pre-defined eligibility criteria derived from the PECO. Conflicts are resolved by consensus or a third reviewer [8] [29].

Step 5: Data Extraction & Categorization Extract study characteristics into a structured, web-based form. For SEMs, extraction typically focuses on design elements (e.g., study type, population, exposure details, outcomes measured) rather than quantitative results. Studies are categorized by key dimensions such as evidence stream (human, animal, NAM), exposure category, and health system assessed [29].

Step 6: Study Evaluation (Optional) For SEMs intended to inform quantitative risk assessment, a study evaluation may be conducted on studies deemed suitable for dose-response analysis. This evaluation typically assesses risk of bias and sensitivity, but not reporting quality, following current best practices [29].

Step 7: Evidence Synthesis & Visualization Synthesize extracted data into an interactive database or dashboard (e.g., using Tableau) [8]. Create visual maps (e.g., heat maps, evidence atlases) showing the distribution and volume of evidence across the defined categories. Generate a narrative summary describing the overall evidence base and identifying key data gaps [29].

Table: Key Metrics and Outputs for Systematic Evidence Maps

Metric Category	Specific Metrics	Purpose in Risk Assessment
Evidence Volume	Number of studies per PECO stream (human, animal); Number of studies per health outcome category.	Identifies research density and potential for quantitative synthesis.
Evidence Distribution	Map of studies by chemical, exposure route, study design (e.g., cohort, chronic bioassay), and model system.	Highlights coverage and identifies critical gaps (e.g., missing exposure routes, susceptible populations).
Study Design & Quality	Summary of study design features (e.g., sample size, exposure assessment method); Results of study evaluation (if performed).	Informs judgments on evidence strength and suitability for dose-response.
NAM & Supplemental Data	Inventory of in vitro, in silico, toxicokinetic, and genomic studies.	Assesses biological plausibility and supports integrated approaches to testing and assessment (IATA).

Experimental Protocols for Evidence Stream Integration

Protocol 4.1: Integrating New Approach Methodologies (NAMs) into Systematic Review

• Objective: To systematically identify, categorize, and evaluate NAM studies for inclusion in a SEM to support mode-of-action analysis and biological plausibility.
• PECO Adaptation for NAMs:
  • Population (P): Specify the in vitro system (e.g., "primary human bronchial epithelial cells," "HepaRG spheroids") or computational model.
  • Exposure (E): Define test article, concentration range, duration, and vehicle.
  • Comparator (C): Define appropriate controls (vehicle, positive control, baseline model).
  • Outcome (O): Define the measured endpoint (e.g., "cell viability," "gene expression of CYP1A1," "model-predicted binding affinity to the estrogen receptor") [30].
• Search Strategy: Incorporate NAM-specific keywords (e.g., "high throughput screening," "transcriptomic," "computational model," "alternative test method") and database sources (e.g., EPA's ToxCast Dashboard).
• Data Extraction: Extract details on the test system, protocol, dose-response metrics, and relevance to human biology. Track adherence to standardized protocols or quality frameworks [31].
• Visualization: Co-visualize NAM data with traditional toxicology data in the SEM dashboard, using linked views to show associations between in vitro pathway activation and in vivo outcomes.

Protocol 4.2: Tiered Data Extraction for Evidence Characterization

• Objective: To efficiently capture essential study characteristics for a large volume of evidence using a tiered, semi-automated approach.
• Tier 1 - Automated Metadata Tagging:
  • Use machine learning or rule-based classifiers in software like DEXTR or SWIFT-Review to tag studies with basic PECO categories from titles/abstracts [29].
  • Tags include: Chemical, Species/System (Human/Rodent/in vitro), Broad Outcome Category (e.g., Hepatic, Neurological).
• Tier 2 - High-Throughput Manual Extraction:
  • Reviewers use a simplified, web-based form to confirm/amend automated tags and extract core design elements: study type (e.g., cross-sectional, chronic bioassay), sample size, exposure duration, and key outcome measured.
• Tier 3 - In-Depth Extraction (for a subset):
  • For studies identified as pivotal (e.g., key dose-response studies), perform detailed extraction of quantitative results, statistical analyses, and individual risk of bias criteria.

The Scientist's Toolkit: Research Reagent Solutions

Table: Essential Tools and Resources for PECO-Driven Evidence Mapping

Tool/Resource Category	Example Solutions	Primary Function in Evidence Mapping
Systematic Review Software	DistillerSR, Rayyan, CADIMA, SWIFT-Review	Manages the screening process, enables dual-independent review, tracks decisions, and provides an audit trail [8] [29].
Data Extraction & Management	DEXTR (semi-automated), Systematic Review Data Repository (SRDR+)	Facilitates structured, consistent data capture from eligible studies into customizable forms; supports collaboration [8].
Chemical Intelligence	EPA CompTox Chemicals Dashboard, PubChem	Provides authoritative chemical identifiers, structures, properties, and related literature to inform search strategies and PECO definitions [29].
Visualization & Dashboarding	Tableau, R (ggplot2, shiny), Python (plotly, dash)	Transforms extracted data into interactive SEMs, heat maps, and evidence gap maps for exploration and communication [8] [33].
NAM Data Repositories	ToxCast/Tox21 Database, LINCS Data Portal, CEBS (Chemical Effects in Biological Systems)	Sources of curated high-throughput screening and genomic data to be captured as supplemental evidence [30].
Protocol & Reporting Guidance	IRIS Handbook, PRISMA-ScR, COSTER	Provides standardized methods and reporting checklists to ensure SEM transparency, quality, and reproducibility [29].

Visualization of an Integrated Risk Assessment Evidence Framework

Diagram: Integrated Risk Assessment Evidence Framework

Implementation and Case Studies

Case Study Application: Applying the Framework to an Emerging Contaminant Consider a SEM for an emerging per- and polyfluoroalkyl substance (PFAS). The PECO is defined broadly: Population (human populations and mammalian models), Exposure (the specific PFAS compound), Comparator (lower/no exposure), Outcome (any health effect). The SEM protocol is executed, identifying 200 relevant studies. The data extraction reveals 80 epidemiological studies, 50 rodent bioassays, 40 in vitro mechanistic studies, and 30 toxicokinetic studies. The visualization shows a strong cluster of evidence for liver effects in rodents and elevated cholesterol in humans, with in vitro data consistently pointing to PPARα activation. The integrated framework allows assessors to see the concordance across evidence streams, strengthening the hypothesis of a causal relationship and identifying the liver as a target organ for dose-response analysis.

Advancing the Field: A Unified Framework for NAMs A significant challenge remains the lack of standardized validation and acceptance criteria for NAMs, hindering their routine use in regulatory risk assessment [31]. The proposed PECO and SEM framework provides a structure for capturing this data. A concerted "call to action" is needed to develop a unified, cross-industry approach to NAMs validation based on measurable quality standards, standardized protocols, and transparent data sharing [31]. By explicitly defining PECO criteria for NAMs and incorporating them into systematic evidence maps, the risk assessment community can accelerate the transition to more human-relevant, efficient, and predictive safety evaluation paradigms [30].

Systematic Search Strategies and Database Selection for Comprehensive Coverage

The field of chemical risk management faces a critical challenge: the need to make transparent, objective, and defensible decisions based on an exponentially growing and heterogeneous body of scientific evidence [2]. Traditional narrative reviews are susceptible to bias and a lack of reproducibility, which can contribute to ambiguity and controversy in risk assessments, as illustrated by cases like bisphenol-A [34]. Systematic review (SR) methods, rigorously developed in healthcare, offer a protocol-driven solution to minimize error and bias when synthesizing evidence [34]. These methods are increasingly being adapted for chemical risk assessment (CRA) by agencies worldwide [34].

However, a core limitation of systematic reviews is their narrow focus on a specific, answerable question [1]. Decision-making in chemicals policy often requires a broader understanding of the evidence landscape to set priorities, formulate problems, and identify knowledge gaps [1] [29]. This is where Systematic Evidence Mapping (SEM) emerges as a foundational tool. An SEM is defined as a queryable database of systematically gathered research that characterizes broad features of an evidence base [1] [2]. It does not perform a quantitative synthesis or meta-analysis but instead provides a comprehensive, interactive overview of available research. Within the context of a thesis on systematic evidence mapping, this document provides the detailed application notes and protocols for the first and most critical step: designing and executing systematic search strategies and selecting databases to achieve comprehensive coverage.

Foundational Protocols for Systematic Evidence Mapping

The cornerstone of a robust SEM is a pre-defined, publicly accessible protocol that minimizes bias and ensures reproducibility. The following workflow outlines the standard phases.

Diagram 1: Systematic Evidence Mapping Workflow. This diagram outlines the six sequential phases for creating a systematic evidence map, from defining the scope to building the final queryable database [29].

Formulating the Research Question and PECO Criteria

Before any search begins, the research question must be framed using a structured format. In environmental health, the PECO framework (Population, Exposure, Comparator, Outcome) is standard [35] [29].

• Population: The organisms or systems studied (e.g., humans, experimental animals like mammalian models, or in vitro systems).
• Exposure: The chemical, physical agent, or mixture of interest. This includes specific chemical names, synonyms, and CASRNs.
• Comparator: The control or reference condition against which exposure is compared (e.g., unexposed group, lower dose group, vehicle control).
• Outcome: The health effects or endpoints measured (e.g., cancer, neurodevelopmental effects, endocrine disruption, mortality).

Example PECO for a PFAS Chemical [35]:

• P: Human populations and mammalian experimental animals.
• E: Exposure to perfluoropropanoic acid (PFPrA, CASRN 422-64-0).
• C: Compared to individuals or groups with lower or no exposure.
• O: Any health outcome.

A broad PECO is typical for an SEM to capture the full evidence landscape. The protocol must also define what constitutes "Supplemental Material," such as in vitro studies, toxicokinetic data, grey literature reports, or studies on non-mammalian models, which are tracked separately [29].

Developing the Systematic Search Strategy

The search strategy aims for high sensitivity (recall) to capture as many potentially relevant records as possible, accepting a lower precision [36] [37].

Key Protocol Steps:

• Identify Search Concepts: Break down the PECO into key concepts (e.g., chemical terms, outcome terms, study type filters).
• Generate Vocabulary: For each concept, compile:
  • Controlled Vocabulary: Database-specific subject headings (MeSH in PubMed, Emtree in Embase).
  • Keywords: Synonyms, trade names, acronyms, and spelling variants. Chemical searches heavily rely on exhaustive name and synonym lists from sources like the EPA CompTox Chemicals Dashboard [35].
• Apply Boolean Operators:
  • OR within concepts to broaden capture.
  • AND between concepts to narrow focus.
• Translate and Adapt: A master strategy is developed in one database (e.g., PubMed) and then meticulously translated for the syntax and vocabulary of each additional database [38].
• Peer Review: The search strategy must be peer-reviewed by an independent information specialist, a formal standard for systematic reviews [36] [37]. The PRESS (Peer Review of Electronic Search Strategies) checklist is a recommended tool [38].

Database Selection and Search Execution

A comprehensive search cannot depend on a single database or bibliographic sources alone [38]. The following table outlines core databases and resources for chemical risk evidence.

Table 1: Essential Databases and Resources for Chemical Risk Evidence Mapping

Resource Category	Specific Resources	Primary Utility in SEM	Search Considerations
Bibliographic Databases	PubMed/MEDLINE [36] [37], Scopus [37], Web of Science [35]	Core source for peer-reviewed journal articles. PubMed is essential for biomedical literature.	Use chemical names, synonyms, and MeSH terms. Web of Science may require targeted strategies to manage result volume [35].
Toxicology-Specific Databases	TOXLINE (via PubMed), Embase [36]	Captures literature in toxicology, pharmacology, and environmental health.	Embase has strong European coverage and unique indexing.
Systematic Review Resources	Cochrane Database of Systematic Reviews [37]	Identifies existing SRs to avoid duplication and find primary studies.
Grey Literature Sources	Regulatory: ECHA REACH Dossiers [35], US EPA HERO [35], US NTP Database [35]Trial Registries: ClinicalTrials.gov [36], EU Clinical Trials Register [36]Theses & Reports: ProQuest Dissertations, Government websites [36]	Captures unpublished, regulatory, and industry data critical for risk assessment to minimize publication bias.	Requires manual search of agency websites and retrieval of dossiers. Citation details may be incomplete [35].
Chemical & Data Hubs	EPA CompTox Chemicals Dashboard (ToxValDB) [35], PFAS-Tox Database [35]	Provides curated chemical properties, synonyms, and aggregated toxicity values from multiple sources.	Data must be verified for accuracy and completeness [35] [29]. Useful for identifying hard-to-find study reports.

Execution Protocol:

• Record Keeping: Log all search dates, exact search strings, and the number of records retrieved from each source in a tracking spreadsheet [38].
• Deduplication: Combine records from all sources into reference management software (e.g., DistillerSR, Rayyan) or a systematic review platform. Use both automated (e.g., by DOI, PMID) and manual processes to remove duplicates [35].
• Record Storage: All references are typically stored in a project database such as the US EPA's Health and Environmental Research Online (HERO) system for permanence and transparency [35].

Study Screening and Data Extraction

This phase follows the PRISMA flow diagram model.

Screening Protocol:

• Title/Abstract Screening: Two independent reviewers assess each record against the PECO criteria. Conflicts are resolved by consensus or a third reviewer [29].
• Full-Text Screening: The same dual-review process is applied to the retrieved full-text articles.
• Machine Learning Assistance: For large evidence bases, tools like SWIFT-Review can prioritize screening by using "evidence streams" to tag records relevant to human, animal, or in vitro research [35].

Data Extraction & Coding Protocol: Structured data extraction forms are used to capture metadata and key study characteristics [8] [29].

• Core PECO Data: Study design, population details, exposure metrics, outcomes measured, results.
• Mapping Codes: Broad, controlled vocabulary codes are applied to categorize studies for the map (e.g., coding "mouse," "rat," and "guinea pig" as "rodent") [2]. This enables high-level querying and visualization.
• Supplemental Material Tagging: Studies not meeting PECO are tagged by category (e.g., "in vitro," "pharmacokinetic model," "grey literature report") [29].

Advanced Implementation: From Data Tables to Knowledge Graphs

Traditional SEMs often use flat, tabular data structures (e.g., spreadsheets), which can be limiting for complex, interconnected chemical risk data [2]. An advanced implementation involves structuring the SEM as a knowledge graph.

Diagram 2: Knowledge Graph Integration for Evidence Mapping. This diagram illustrates how extracted study data is linked to formal ontologies to build a flexible, semantically rich knowledge graph, moving beyond rigid table structures [2].

A knowledge graph represents entities (studies, chemicals, outcomes) as nodes and their relationships as edges. This schemaless, on-read approach is more flexible than predefined tables for handling heterogeneous data [2]. By integrating formal ontologies (controlled, logically related vocabularies) like chemical (ChEBI) or disease (DOID) ontologies, the SEM becomes interoperable and semantically powerful, enabling complex queries about mechanistic pathways or chemical classes.

Table 2: Research Reagent Solutions for Systematic Evidence Mapping

Tool Category	Specific Tool / Resource	Function in SEM Protocol
Protocol & Project Management	DistillerSR [8], Rayyan, CADIMA	Web-based platforms for managing the entire SR/SEM process: screening, extraction, and consensus.
Search Strategy Development	PubMed PubReMiner [38], Yale MeSH Analyzer [38], Polyglot Search Translator [38]	Aids in identifying key search terms, analyzing MeSH usage in seed articles, and translating strategies between databases.
Deduplication	"Deduper" tools (e.g., ICF's Python-based tool) [35], built-in functions in Rayyan/DistillerSR	Employs fuzzy matching and machine learning to identify and remove duplicate records from multiple database searches.
Machine Learning / Screening Prioritization	SWIFT-Review [35], RobotSearch	Uses active learning to prioritize records for screening based on relevance, significantly accelerating the title/abstract phase for large datasets.
Data Extraction & Curation	Semi-automated extraction tools, DEXTR [8], HAWC (Health Assessment Workspace Collaborative)	Facilitates structured data extraction into forms. Some tools use NLP to pre-populate fields. HAWC is specifically designed for health assessment data.
Visualization & Database Creation	Tableau [8], R Shiny, Python libraries (Plotly, NetworkX)	Creates interactive, queryable visualizations and dashboards from the coded SEM data to present evidence gaps and clusters.
Grey Literature Search	Grey Matters (CADTH) [36], Think Tank Search (Harvard) [36], OpenGrey	Targeted search tools and repositories to locate hard-to-find reports, theses, and regulatory documents.

Application Notes and Data Presentation

The final output of the SEM is a structured database and a set of visualizations that present the characteristics of the evidence base. Key outputs include:

• PRISMA Flow Diagram: Documents the flow of records through screening.
• Evidence Inventory Tables: Summarize the volume and type of evidence.

Table 3: Sample Evidence Inventory from a Hypothetical SEM on "Chemical X"

Evidence Stream	PECO-Relevant Studies	Supplemental Material Studies	Key Health Outcomes Identified (Top 3)
Human Epidemiological	45	22 (Exposure-only)	Hepatic disease, Thyroid dysfunction, Developmental delay
Mammalian In Vivo	128	18 (Toxicokinetics)	Liver weight increase, Altered serum hormones, Neurobehavioral effects
In Vitro	N/A	305	Cytotoxicity, Receptor binding, Genotoxicity
Grey Literature / Regulatory	12 (from ECHA)	8 (from NTP)	Various systemic effects

• Interactive Evidence Maps: Visualizations, often heatmaps or bubble plots, showing the intersection of exposures, outcomes, and study types, highlighting dense clusters of research and clear evidence gaps [8].
• Narrative Summary: A brief report describing the overall evidence landscape, major trends, and critical data gaps to inform problem formulation and priority setting for future research or risk assessment [29].

By following these detailed protocols for systematic searching and database selection, researchers can construct a robust, transparent, and comprehensive systematic evidence map. This map serves as the critical foundation for informed decision-making in chemical risk management, guiding efficient resource allocation for deeper systematic reviews and targeted primary research.

Leveraging Machine Learning and Software Tools for Efficient Study Screening

Systematic evidence mapping (SEM) has emerged as a critical evidence-based methodology for supporting decision-making in chemical policy and risk management. Unlike systematic reviews, which answer narrowly focused questions, SEMs provide comprehensive, queryable databases of research, characterizing broad features of an evidence base to inform priority-setting and trend identification [1]. This approach is particularly valuable in environmental health and toxicology, where decision-makers face broad, multifaceted information needs that cannot be met by a single systematic review [2].

The exponential growth of available scientific data on chemical hazards and exposures presents both an opportunity and a challenge. While more data can potentially lead to more informed decisions, the sheer volume makes traditional manual screening and synthesis methods prohibitively resource-intensive and slow [2]. This creates a pressing need for more efficient, scalable methodologies. Machine learning (ML) and specialized software tools offer a transformative solution by automating labor-intensive tasks, such as literature screening and data extraction, and by enabling the sophisticated analysis of complex, interconnected data [39]. This document details the application notes and protocols for integrating these technologies into SEM workflows, framing them within a broader thesis on systematic evidence mapping for chemical risk management research.

Core Machine Learning Applications in Study Screening and Risk Assessment

Machine learning models are revolutionizing the efficiency and predictive power of chemical risk assessment. Their applications range from automating screening workflows to predicting complex hazard endpoints.

Automated Literature Screening and Prioritization: Active learning models, such as those implemented in tools like SWIFT-Review, can dramatically accelerate the study screening process. These models iteratively learn from human decisions, prioritizing documents that are most likely to be relevant for full-text review. This can reduce the manual screening workload by 50-70% while maintaining high sensitivity and specificity [39]. This semi-automated, "human-in-the-loop" approach ensures scalability without sacrificing the rigor required for systematic methods.

Predictive Modeling of Chemical Hazards: Supervised ML models excel at predicting key hazardous properties from chemical structure data. Recent studies demonstrate the superior performance of tree-based ensemble methods:

• XGBoost achieved high performance in predicting chemical toxicity (ROC-AUC: 0.768) and reactivity (ROC-AUC: 0.917) [40].
• Random Forest excelled in predicting flammability (ROC-AUC: 0.952) and reactivity with water (ROC-AUC: 0.852) [40].

Interpretability techniques like SHAP (Shapley Additive exPlanations) analysis are crucial for regulatory acceptance, as they identify the key molecular descriptors (e.g., MIC4, ATSC2i) driving predictions, moving beyond "black-box" models [40].

Exposure and Risk Prediction: ML enhances traditional exposure models like the Advanced Reach Tool (ART). Deep neural networks trained on measurement data can provide more accurate exposure predictions. To overcome data scarcity, a promising pipeline involves generating synthetic training datasets from existing models, enabling robust ML model development where empirical data is limited [41]. Furthermore, models integrating multi-omics data (genomics, transcriptomics) with chemical information show great promise for elucidating mechanisms of carcinogenesis and predicting genotoxic risk [42].

Table: Performance Metrics of ML Models for Hazard Prediction [40]

Hazard Endpoint	Best Performing Model	Key Metric (ROC-AUC)	Key Interpreted Molecular Descriptors
Toxicity	XGBoost	0.768	MIC4, ATS4i
Flammability	Random Forest (RF)	0.952	ATSC2i
Reactivity	XGBoost	0.917	ETAdEpsilonC
Reactivity with Water	Random Forest (RF)	0.852	ETAdEpsilonC

Essential Software Tools and Data Infrastructure

The effective implementation of an ML-augmented SEM requires a stack of interoperable software tools for data management, processing, and visualization.

Data Management with Knowledge Graphs: Traditional databases using rigid, flat schemas struggle with the highly connected and heterogeneous nature of toxicological data (e.g., linking chemicals, studies, endpoints, and models). Knowledge graphs offer a superior, flexible solution. They use a schemaless, graph-based structure where entities (nodes) and relationships (edges) can be dynamically added, making them ideal for integrating diverse data sources [2]. This supports schema-on-read, allowing data to be structured according to the needs of the specific query or analysis, which is vital for exploring complex evidence maps.

Specialized Screening and Extraction Tools:

• SWIFT-Active Screener: Accelerates title/abstract screening via active learning, continuously refining its predictions based on reviewer feedback [39].
• Dextr: A semi-automated data extraction tool that uses ML to identify and extract specific data fields (e.g., dosage, outcomes) from full-text articles, validating extractions through a human-in-the-loop interface [39].

Visualization and Analysis Platforms: Effective communication of SEM findings relies on robust visualization.

• HAWC (Health Assessment Workspace Collaborative): A central platform for managing and visualizing data from human health assessments, supporting data extraction and trend analysis [39].
• Interactive Visualization Tools: Libraries like D3.js and Plotly enable the creation of custom, interactive dashboards for exploring evidence maps, showing study distributions, chemical clusters, or evidence gaps [43].
• Network Visualization: Tools like Cytoscape and Gephi are essential for visualizing and analyzing the complex relationships within knowledge graphs, such as chemical-study networks [43].

Table: Key Software Tools for ML-Augmented Evidence Mapping

Tool Category	Example Tools	Primary Function in SEM Workflow	Access Type
Screening Automation	SWIFT-Active Screener, Rayyan	Prioritizes and classifies references for manual review using active learning.	Desktop/Web-based
Data Extraction	Dextr, HAWC Client	Performs semi-automated extraction of structured data from full-text papers.	Web-based/API
Data Management & Storage	Graph Databases (Neo4j), HAWC	Stores interconnected data as knowledge graphs for flexible querying.	Server/Web-based
Programming & Analysis	Python (Pandas, Scikit-learn), R	Provides environment for building custom ML models and data analysis.	Open-source
Visualization	Tableau, Plotly, Cytoscape	Creates static and interactive charts, graphs, and network diagrams.	Desktop/Web-based

Detailed Experimental Protocols

Protocol for Systematic Screening with ML Prioritization

This protocol follows the template established by the U.S. EPA IRIS Program [3] and integrates ML for efficiency.

Objective: To identify and screen all potentially relevant mammalian bioassay and epidemiological studies for a target chemical or chemical class.

Materials & Software:

• Bibliographic databases (PubMed, Web of Science, Embase).
• Reference management software (e.g., EndNote, Zotero).
• Active learning screening software (e.g., SWIFT-Active Screener).
• EPA SEM template and PECO (Population, Exposure, Comparator, Outcome) criteria [3].

Procedure:

• Question Formulation & Search:
  • Define a broad PECO statement. Example: (P) Humans or mammalian models, (E) Exposure to chemical X, (C) Unexposed or differently exposed controls, (O) Any health effect [3].
  • Execute a comprehensive search string across multiple databases. Remove duplicates.

• ML-Aided Title/Abstract Screening:

  • Import all references into the active learning software.
  • A minimum of two reviewers independently screen a random seed set of 200-300 references, marking them as "Include" or "Exclude."
  • The ML model trains on these decisions and scores the remaining references for relevance probability.
  • Reviewers screen the ML-prioritized list (highest probability first). The model continuously retrains after each batch of decisions.
  • Screening stops when a pre-set stopping rule is met (e.g., 100 consecutive excludes). The remaining low-probability references are reviewed with minimal manual effort [39].

• Full-Text Review & Data Inventory:

  • Retrieve full texts of included records.
  • Extract and inventory high-level metadata (study design, species, exposure regimen, endpoints assessed) into a structured form in a system like HAWC. This creates the core queryable database of the SEM [3].

Diagram: Systematic Screening Workflow with Active Learning

Protocol for Semi-Automated Data Extraction

This protocol is based on a proof-of-concept study for using ML (Dextr) to extract specific data fields from full-text studies [39].

Objective: To accurately extract structured data (e.g., dosage groups, mean response, standard deviation) from included studies with greater efficiency than fully manual extraction.

Materials & Software:

• Full-text PDFs of included studies.
• Semi-automated data extraction tool (e.g., Dextr).
• Pre-defined, structured data extraction forms.

Procedure:

• Model Setup and Training:
  • Configure the extraction tool with the target data fields and their expected format.
  • For a subset of studies, perform manual extraction to create a gold-standard training set.

• Semi-Automated Extraction (Human-in-the-Loop):

  • The ML model processes new PDFs, identifying and proposing extractions for target fields.
  • A human reviewer is presented with the model's predictions in context (e.g., highlighted text in the PDF).
  • The reviewer verifies, corrects, or rejects each proposed extraction. These corrections are fed back to the model to improve subsequent performance.

• Validation and Quality Control:

  • Compare a sample of ML-assisted extractions against a fully manual extraction performed independently.
  • Calculate precision, recall, and F1 scores to validate that the semi-automated approach meets quality thresholds [39].
  • Resolve any discrepancies through expert consensus.

Protocol for Predictive Model Development and Integration

This protocol outlines steps for developing an ML model to predict hazard properties, integrating it into an SEM for priority setting [40] [41].

Objective: To develop an interpretable ML model that predicts a specific hazard (e.g., acute oral toxicity) for chemicals within an evidence map, identifying data-poor chemicals that may be high risk.

Materials & Software:

• Curated dataset of chemicals with known hazard properties (e.g., from EPA's CompTox Dashboard).
• Molecular descriptor calculation software (e.g., RDKit, PaDEL).
• ML programming environment (Python/R).
• Model interpretation libraries (SHAP, LIME).

Procedure:

• Data Curation and Feature Engineering:
  • Curate a dataset of chemical structures (SMILES) and associated hazard labels.
  • Generate molecular descriptors (e.g., topological, electronic, geometrical) as model features.
  • Address class imbalance using techniques like SMOTE or under-sampling.

• Model Training and Optimization:

  • Split data into training, validation, and test sets.
  • Train multiple algorithms (e.g., Random Forest, XGBoost, Neural Networks).
  • Optimize hyperparameters using cross-validation.
  • Select the best model based on the ROC-AUC score on the held-out test set.

• Interpretation and SEM Integration:

  • Apply SHAP analysis to the best model to identify the molecular features most influential for prediction.
  • Use the trained model to predict hazards for chemicals in the SEM that lack experimental data.
  • Within the SEM visualization dashboard, filter and rank chemicals by predicted hazard to guide future research or assessment priorities.

Diagram: ML Model Development and Integration into SEM Workflow

The Scientist's Toolkit: Essential Research Reagent Solutions

Table: Key Reagents and Materials for Computational Risk Assessment Workflows

Item Name	Function / Role in Workflow	Specifications / Notes
Chemical Identifier Databases	Provides standardized structural data (SMILES, InChIKeys) for model input.	PubChem, EPA CompTox Dashboard. Essential for curating training sets.
Molecular Descriptor Software	Calculates quantitative features from chemical structure used as ML model input.	RDKit (open-source), PaDEL-Descriptor. Generates 1D-3D descriptors.
Curated Toxicity Datasets	Provides high-quality, structured experimental data for supervised ML model training.	ToxCast/Tox21 database, ECHA classification data. Must be carefully curated for endpoint consistency.
Graph Database System	Stores and queries the interconnected data of the Systematic Evidence Map.	Neo4j, Amazon Neptune. Enables efficient traversal of chemical-study-outcome relationships [2].
High-Performance Computing (HPC) or Cloud Instance	Provides the computational resources for training complex ML models on large datasets.	Cloud platforms (AWS, GCP) or institutional clusters. GPU acceleration recommended for deep learning.
Model Interpretation Library	Explains model predictions, identifying key contributing features for regulatory acceptance.	SHAP (SHapley Additive exPlanations), LIME. Critical for moving beyond "black box" models [40].

Data Extraction and Coding Techniques for Hazard Characterization

Effective chemical risk management requires synthesizing vast and heterogeneous data to predict and prevent harm. Traditional, manual methods are increasingly inadequate given the scale of existing and new chemicals in commerce [44]. Systematic Evidence Maps (SEMs) have emerged as a critical tool to address this challenge, providing a comprehensive, queryable overview of a broad evidence base to inform priority-setting and decision-making [6]. Unlike a Systematic Review (SR), which synthesizes evidence to answer a tightly focused question, an SEM characterizes the extent and nature of available research, identifying key knowledge clusters and gaps [6] [45].

The construction of a robust SEM is fundamentally dependent on advanced data extraction and coding techniques. These techniques transform unstructured or semi-structured data—from scientific literature, regulatory documents, and experimental databases—into structured, computable formats. This process enables the high-throughput analysis necessary for modern chemical safety assessment. As highlighted by the U.S. EPA's transformation of its research portfolio, there is a pressing need for "chemical exposure foresight for thousands of chemicals at a time" [44]. Automating the extraction of hazard data is therefore not merely an efficiency gain but a prerequisite for proactive risk management frameworks like REACH and TSCA [6].

This article details contemporary methodologies for extracting and coding hazard characterization data, framing them as essential protocols within the broader workflow of developing an SEM for chemical risk management.

Data Extraction Techniques: From Documents to Structured Data

The initial step in hazard data curation is the conversion of information locked in documents into structured fields. Safety Data Sheets (SDS) are a primary source, but manual indexing is resource-intensive [44]. Automated information extraction systems, particularly those using machine learning (ML), are now achieving precision necessary for commercial and regulatory application.

2.1 Machine Learning-Driven SDS Indexing A state-of-the-art system for "standard indexing" employs a multi-step pipeline combining ML models and expert rules to extract five key fields: Product Name, Product Code, Manufacturer Name, Supplier Name, and Revision Date [44]. The pipeline involves:

• Document Pre-processing: Conversion of PDF SDS to text and image regions.
• Field Localization: Identification of text blocks containing target information using layout analysis.
• Named Entity Recognition (NER): Application of sequence labeling models (e.g., Bidirectional Encoder Representations from Transformers (BERT)) to classify and extract specific entities from the text [44].
• Validation & Post-processing: Application of domain-specific rules to validate extracted entities.

This system reported a precision of 0.96–0.99 across fields when evaluated on 150,000 annotated SDS documents [44]. The table below compares this approach with other documented methods.

Table 1: Comparison of Automated Information Extraction Techniques for Chemical Documents

Method	Description	Reported Performance (Precision/Accuracy)	Key Advantage	Primary Limitation
Multi-step ML & Expert System [44]	Hybrid pipeline using BERT-based NER and rules.	0.96 – 0.99	High precision suitable for regulatory/commercial use.	Requires significant annotated data for training.
Regular Expressions [44]	Pattern-matching rules on text.	0.35 – 1.00	Very high accuracy on perfectly structured text.	Performance collapses on unseen or unstructured formats.
Traditional ML (Decision Trees) [44]	Models using hand-crafted features.	Precision: 0.77, Recall: 0.65	Simpler to implement than deep learning.	Lower performance; requires extensive feature engineering.
ID-CNN for NER [44]	Iterated Dilated Convolutional Neural Networks.	Comparable to LSTM-CRF	14-20x faster test-time than LSTM-based models.	Less effective on very long-range text dependencies.

2.2 Feature Extraction from Complex Datasets Beyond text, hazard characterization increasingly integrates data from high-throughput screening (HTS), physicochemical sensors, and even cybersecurity monitors in industrial settings [46]. Feature extraction methods like Principal Component Analysis (PCA) are vital for reducing dimensionality and identifying the most discriminatory signals.

An advanced method improves upon the classical Kaiser criterion by determining the number of principal components based on their discriminant power for a specific classification task (e.g., chemical risk level) [46]. This approach, when classifying chemical hazard risk using sensor and network anomaly data, improved classification quality by ~7% compared to using no feature extraction and by ~4% compared to standard PCA [46]. Key cybersecurity features affecting risk assessment included packet loss and incorrect sensor responses [46].

Coding Protocols for Hazard Data: Structuring Evidence for Analysis

Once extracted, data must be coded into standardized formats to enable aggregation, analysis, and visualization within an SEM.

3.1 Protocol for Systematic Evidence Mapping The SEM methodology provides a structured framework for coding literature-based evidence [6] [45]. Key steps include:

• Define a Broad PECO Framework: The Population, Exposure, Comparator, Outcome criteria are kept broad to capture all potentially relevant mammalian bioassay and epidemiological studies [45].
• Comprehensive Search & Screening: Use systematic searches across multiple databases. Machine learning "sifter" tools can aid in triaging search results [47].
• Structured Data Extraction: Code included studies into a standardized web-based form. For an SEM, extraction is descriptive rather than analytical, capturing study design, test system, exposure parameters, and health systems assessed [45].
• Supplemental Evidence Tracking: Code the presence of supplementary data types (e.g., in vitro assays, toxicokinetics, New Approach Methods (NAMs)) to map the broader evidence landscape [45].

Table 2: Core Data Elements for Coding Studies in a Hazard Characterization SEM

Data Category	Specific Fields to Code	Purpose & Notes
Study Identification	Citation, DOI, Funding source.	Traceability and bias assessment.
Chemical & Exposure	Chemical name/CASRN, dose/conc., route, duration.	Enables grouping and dose-response analysis.
Test System	Species, strain, sex, age, cell line, model type (in vivo/in vitro/NAM).	Informs relevance and biological applicability.
Experimental Design	Control type, group size, randomization, blinding.	Critical for later quality assessment.
Outcomes & Effects	Endpoint measured (e.g., liver weight, gene expression), effect direction & magnitude, significance.	Core hazard data for mapping.
Reporting Quality	Adherence to guidelines (e.g., OECD, ARRIVE), data completeness.	Supports confidence in evidence.

3.2 Coding for Predictive Model Development For data driving predictive hazard models, coding must facilitate computational analysis. This involves:

• Chemical Identifiers: Standardizing on unique identifiers (e.g., DSSTox Substance IDs (DTXSID)) to unify data from disparate sources [47].
• Assay Endpoints: Coding HTS results using standardized biological pathway ontologies (e.g., from ToxCast) [47].
• Dose-Response Data: Structuring quantitative outcomes in a machine-readable format for benchmark dose (BMD) modeling or points of departure derivation.

Experimental Protocols for Integrated Hazard Analysis

4.1 Protocol: Building a Predictive Model for Natural Hazard-Triggered Chemical Incidents (Natechs) Objective: To develop a machine learning classifier that predicts high-risk days for chemical emission incidents based on climate data [48]. Dataset: Time-series data linking daily climate variables (precipitation, lightning, wind speed, temperature) with chemical emission incident reports from an industrial region (e.g., Houston, TX) over 20 years [48]. Procedure:

• Data Labeling: Code each calendar day as a binary outcome: "high-risk" (days with one or more emission incidents) or "low-risk" (days with no incidents) [48].
• Feature Engineering: Calculate rolling averages (e.g., 7-day average precipitation) and event thresholds (e.g., days with lightning) from raw climate data.
• Model Training & Selection: Split data into training/validation sets. Train multiple classifier algorithms (e.g., XGBoost, Random Forest, SVM). Optimize hyperparameters using cross-validation.
• Conformal Prediction Wrapper: Implement a conformal inference framework to control the model's error rate and provide guaranteed sensitivity or specificity levels, crucial for risk-averse decision-making [48].
• Validation & Interpretation: Evaluate final model performance using the area under the receiver operating characteristic curve (ROC AUC) and calibrate thresholds. Use SHAP (SHapley Additive exPlanations) analysis to interpret feature importance (e.g., identifying lightning and precipitation as top contributors) [48].

4.2 Protocol: High-Throughput Hazard Characterization Using Public Toxicity Databases Objective: To perform a rapid hazard profile for a list of chemicals using pre-extracted and coded data from public repositories. Data Sources:

• ToxValDB (v9.6): Aggregates over 237,000 in vivo toxicity records for nearly 40,000 chemicals, providing standardized toxicity values and study summaries [47].
• ToxCast Dashboard: Provides high-throughput screening bioactivity data for ~9,000 chemicals across hundreds of assay endpoints [47].
• ECOTOX: Supplies ecotoxicological effects data for aquatic and terrestrial species [47]. Procedure:

• Chemical List Standardization: Query the CompTox Chemicals Dashboard to resolve chemical names to DTXSIDs.
• Batch Data Retrieval: Use Application Programming Interfaces (APIs) or downloadable data files to pull all relevant records for the target DTXSIDs from ToxValDB, ToxCast, and ECOTOX.
• Data Integration & Coding: Merge datasets using DTXSID. Code the "most sensitive endpoint" and its corresponding point of departure (POD) from ToxValDB for each chemical. Code ToxCast activity flags for key toxicity pathways (e.g., estrogen receptor antagonism).
• Hazard Triaging: Apply decision rules to rank chemicals. For example: High Priority = (POD < 1 mg/kg_bw/day) OR (Active in >50% of nuclear receptor assays).

Visualization of Workflows and Data Relationships

Systematic Evidence Mapping and Data Integration Workflow

Machine Learning Pipelines for Data Extraction and Hazard Prediction

Table 3: Key Research Reagent Solutions and Data Resources

Resource Name	Type	Primary Function in Hazard Characterization	Source/Access
CompTox Chemicals Dashboard	Database & Tool	Central hub for chemical identifiers, properties, and linked toxicity data. Crucial for standardizing chemical lists.	U.S. EPA [47]
ToxValDB (v9.6+)	Aggregated Database	Provides pre-extracted, standardized in vivo toxicity values and study summaries for rapid hazard profiling.	U.S. EPA [47]
ToxCast Data	High-Throughput Screening Data	Bioactivity profiles across ~900 assays. Used for mechanism-based hazard identification and pathway modeling.	U.S. EPA [47]
ECOTOX Knowledgebase	Ecotoxicology Database	Provides curated data on chemical effects for aquatic and terrestrial species for ecological risk assessment.	U.S. EPA [47]
Abstract Sifter	Literature Mining Tool	Excel-based tool to triage and prioritize PubMed search results using relevance ranking, aiding SEM development.	U.S. EPA [47]
BERT or Similar LLM Models	Machine Learning Model	Pre-trained language models fine-tuned for Named Entity Recognition (NER) to extract specific data fields from text.	Open-source (e.g., Hugging Face)
XGBoost / Random Forest Libraries	Machine Learning Library	Libraries for building high-performance classifiers for predictive risk modeling, as demonstrated in Natech research [48].	Open-source (e.g., scikit-learn, XGBoost)

Creating Interactive Evidence Maps and Visual Dashboards for Stakeholder Use

In the domain of chemical risk management, researchers and regulators are tasked with making critical decisions based on vast, fragmented, and rapidly expanding evidence bases. Systematic Evidence Maps (SEMs) have emerged as a pivotal methodology to address this challenge, offering a structured, transparent approach to cataloging and organizing scientific literature [16]. Unlike a systematic review, which synthesizes evidence to answer a specific question, an SEM characterizes the broader landscape of available research, identifying trends, clusters of activity, and critical knowledge gaps [6]. This process is foundational for priority-setting, informing targeted systematic reviews, and guiding future primary research [16].

The transition from a static evidence map to an interactive visual dashboard represents a significant advancement in utility for stakeholders. Dashboards transform mapped evidence into a dynamic, queryable interface, enabling real-time exploration and decision-making. This integration is particularly valuable for regulatory initiatives like the U.S. EPA’s Toxic Substances Control Act (TSCA) assessments, where evolving evidence must be continuously monitored and assessed [49]. This document provides detailed application notes and protocols for creating these integrated tools within the context of chemical risk management research.

Foundational Methodology: The Systematic Evidence Map (SEM) Workflow

The creation of a robust, dashboard-ready SEM follows a rigorous, multi-stage protocol. The following workflow, adapted from standardized templates such as those used by the U.S. EPA’s Integrated Risk Information System (IRIS), ensures comprehensiveness, reproducibility, and transparency [45].

SEM Protocol: Key Stages and Outputs

Stage	Primary Objective	Key Activities	Software/Tool Examples	Output for Dashboard
1. Problem Formulation & Protocol	Define the scope and methodology.	Develop a PECO statement; write and register a public protocol.	–	Published protocol; defined data fields.
2. Systematic Search	Identify all potentially relevant evidence.	Search multiple bibliographic databases, grey literature; document search strategy.	PubMed, Web of Science, Scopus	Raw literature inventory.
3. Screening & Selection	Filter studies against eligibility criteria.	Title/abstract and full-text screening, typically by two independent reviewers.	Rayyan, Covidence, SWIFT-Review	Final list of included studies.
4. Data Extraction & Coding	Characterize each study systematically.	Extract metadata (e.g., chemical, study type, model system, outcomes) into a structured form.	CADIMA, HAWC, custom web forms	Coded database (e.g., CSV, JSON).
5. Evidence Mapping & Categorization	Organize and classify the evidence base.	Categorize studies by dimensions of interest (e.g., health effect, evidence stream).	Python/R scripts, Excel PivotTables	Matrices, heatmaps, relational data.
6. Study Evaluation (Optional)	Assess certain study characteristics.	Apply risk-of-bias or quality checks on a case-by-case basis [45].	ROBINS-I, NTP/OHAT tool	Quality ratings for relevant studies.

The PECO (Population, Exposure, Comparator, Outcome) criteria are typically kept broad to capture a wide range of mammalian animal bioassays and epidemiological studies. Supplemental tracking of New Approach Methodologies (NAMs)—including high-throughput in vitro assays, transcriptomics, and in silico models—is also a critical component for modern chemical assessment [45].

Diagram 1: Systematic Evidence Map (SEM) Creation Workflow

Dashboard Design: From Static Maps to Interactive Tools

An interactive dashboard is the user-facing component that unlocks the value of the SEM database. Its design must be driven by stakeholder needs, transforming raw data into actionable insights for decision-making [50].

Core Dashboard Components & Chemical Risk KPIs

A dashboard for chemical risk evidence should centralize key performance indicators (KPIs) that speak to the completeness, quality, and distribution of the evidence base. These differ from commercial KPIs and are tailored to research assessment.

Key Dashboard Components and Chemical Risk KPIs

Dashboard Component	Description	Example Chemical Risk KPIs & Metrics
Evidence Overview	High-level summary of the mapped evidence.	Total studies; count by evidence stream (in vivo, epidemiological, in vitro NAMs); yearly publication trend.
Evidence Gap Heatmap	Visual matrix revealing research density.	Number of studies per chemical/chemical class vs. health outcome (e.g., hepatotoxicity, carcinogenicity).
Study Characteristics Panel	Details on study design and quality.	Distribution by study type (e.g., cohort, chronic bioassay); risk-of-bias rating summary; species/model system used.
Chemical Priority Filter	Interactive controls to drill down.	Filters by chemical (e.g., vinyl chloride, benzene [49]), CAS number, regulatory status (e.g., TSCA priority [49]), or use category.
Evidence Stream Network	Shows relationships between chemicals and outcomes.	Interactive network graph linking chemicals, shared molecular targets, and common adverse outcome pathways.

Visualization and Accessibility Standards

Effective visualizations are critical for communication. Adherence to accessibility standards ensures usability for all stakeholders, including those with visual impairments.

• Color Contrast: All text and non-text elements (like graph lines) must meet WCAG 2.2 guidelines. For normal text, the minimum contrast ratio against the background is 4.5:1 (Level AA). For large text (≥18 pt), it is 3:1 [51] [52].
• Color Palette: Use a consistent, accessible palette. The specified colors (e.g., #4285F4 blue, #EA4335 red, #34A853 green) should be applied to data categories, with sufficient contrast against backgrounds (white #FFFFFF or light grey #F1F3F4) [52]. Avoid using color as the sole means of conveying information.
• Interactivity: Core features include tooltips (revealing study details on hover), click-to-filter actions, and dynamic linking between visualizations (brushing) [50] [53].

Implementation Protocol: Building the Integrated System

This protocol outlines a scalable, automated architecture for moving from evidence synthesis to a live dashboard, minimizing manual effort.

Phase 1: Data Pipeline Automation Objective: Create a repeatable process for updating the evidence database.

• Containerized Extraction Scripts: Package literature search, screening, and data extraction scripts (e.g., in Python) using Docker to ensure environment consistency.
• Orchestrated Workflow: Use a workflow manager (e.g., Apache Airflow, Nextflow) to automate the sequence: triggering searches, running screening algorithms, and populating the central database.
• Central Evidence Warehouse: Store the cleaned, structured data from the SEM in a dedicated database (e.g., PostgreSQL, cloud data warehouse) that serves as the single source of truth [50].

Phase 2: Dashboard Development & Deployment Objective: Build a maintainable and interactive front-end application.

• Backend API: Develop a RESTful API (e.g., using FastAPI or Django) that queries the evidence warehouse. This layer handles business logic, filtering, and data aggregation.
• Frontend Visualization: Build the dashboard interface using a modern web framework (e.g., React, Vue.js) integrated with visualization libraries like D3.js for custom plots or Plotly for interactive charts.
• Deployment: Deploy the application as a web service using cloud providers (AWS, GCP, Azure) or container orchestration (Kubernetes). Ensure secure access for stakeholders.

Diagram 2: Integrated Dashboard System Architecture

The Scientist's Toolkit: Essential Research Reagent Solutions

Building and maintaining an interactive evidence mapping system requires a suite of specialized software and services. The following toolkit is categorized by function.

Essential Software & Tools for Interactive Evidence Mapping

Category	Tool Name	Primary Function in SEM/Dashboard Workflow	Key Consideration
Literature Management	Rayyan, Covidence	Supports blinded collaborative screening of titles/abstracts and full texts during the SEM process.	Reduces human error and improves reproducibility of study selection.
Machine Learning Screening	SWIFT-Review, ASReview	Uses active learning to prioritize potentially relevant records during screening, increasing efficiency [45].	Requires an initial seed of relevant studies; performance is topic-dependent.
Data Extraction & Coding	CADIMA, HAWC (Health Assessment Workspace Collaborative)	Provides structured web forms for consistent data extraction and facilitates evidence mapping [45].	HAWC is specifically designed for health assessment, aligning well with chemical risk.
Workflow Orchestration	Apache Airflow, Nextflow	Automates and schedules the multi-step SEM data pipeline (search, process, update database).	Essential for maintaining a "living" dashboard with periodic evidence updates.
Data Visualization & BI	Plotly (Dash), Tableau, Power BI	Creates the interactive dashboard front-end with charts, graphs, and filters [50] [53].	Plotly Dash offers deep customization for complex scientific data; Tableau/Power BI may be faster for standard charts.
Accessibility Testing	WAVE, Colour Contrast Analyser (CCA)	Audits the dashboard interface for WCAG compliance, specifically checking color contrast ratios [52].	Must be used throughout front-end development, not just as a final check.

Case Application: TSCA Chemical Risk Evaluations

The U.S. EPA’s ongoing work under the Toxic Substances Control Act (TSCA) provides a relevant case study. The EPA has begun risk evaluations for chemicals like vinyl chloride (a known human carcinogen) and acrylonitrile (a probable human carcinogen), while initiating prioritization for others like benzene and styrene [49].

An interactive evidence dashboard for this initiative would:

• Map the Evidence: Visually display the volume and type of available studies (epidemiology, animal bioassay, in vitro) for each priority chemical.
• Highlight Gaps: Identify health outcomes or exposure scenarios for which little data exists, guiding the scope of the required risk evaluation.
• Track Progress: Allow regulators and the public to monitor the evidence collection and assessment status for multiple chemicals in parallel.
• Inform Scope: Use evidence clusters to decide whether a full systematic review is warranted for a specific chemical-outcome pair [6].

The EPA has noted its use of "interactive literature inventory trees and evidence maps" to improve transparency in its systematic review process, underscoring the practical adoption of these methods [49].

The integration of Systematic Evidence Mapping with interactive visual dashboards creates a powerful, living tool for chemical risk management. This approach moves beyond static PDF reports to a dynamic, queryable evidence system that supports priority-setting, efficient resource allocation for systematic reviews, and transparent stakeholder engagement. By following the standardized protocols, design principles, and implementation strategies outlined here, research teams can construct robust platforms that transform fragmented data into a clear foundation for evidence-informed decision-making. As regulatory science evolves, these tools will be critical for managing the growing body of evidence on both legacy and emerging chemical substances.

Overcoming SEM Challenges: Optimization Strategies for Researchers

Addressing Methodological Inconsistencies and Bias Risks in Evidence Mapping

Systematic Evidence Maps (SEMs) are defined as queryable databases of systematically gathered research that characterize broad features of an evidence base [6]. In the context of chemical risk management, they serve as a critical tool for organizing, analyzing, and exploring trends across a large and complex body of scientific literature on health risks posed by chemical exposures [1]. Unlike systematic reviews, which aim to synthesize evidence to answer a tightly focused question, SEMs provide a comprehensive overview that supports priority-setting, identifies evidence gaps, and informs the efficient deployment of more resource-intensive systematic reviews [6] [2].

The methodology is particularly valuable for regulatory initiatives like EU REACH and US TSCA, where decision-makers face an overwhelming volume of data on legacy and new chemicals [6]. However, the process of creating these maps is susceptible to methodological inconsistencies and biases that can compromise their reliability and utility. This document outlines the key sources of these issues and provides detailed protocols and application notes for mitigating them, ensuring SEMs serve as a robust foundation for evidence-based decision-making.

Identifying and Characterizing Key Methodological Inconsistencies

The construction of an SEM involves multiple steps where inconsistency can be introduced. The table below summarizes the major phases, common inconsistencies, and their potential impact on the map's output.

Table 1: Major Sources of Methodological Inconsistency in Evidence Mapping

Mapping Phase	Common Inconsistencies	Impact on Evidence Map
Search Strategy Development	Varying search strings across reviewers; inconsistent use of databases and grey literature sources [6].	Results in an unrepresentative evidence base, missing key studies and compromising comprehensiveness.
Study Screening & Eligibility	Subjective interpretation of Population-Exposure-Comparator-Outcome (PECO) criteria; lack of calibrated dual-reviewer process [6].	Introduces selection bias, where studies are included or excluded based on reviewer judgment rather than predefined rules.
Data Extraction & Coding	Use of non-standardized, ad hoc extraction forms; inconsistent application of controlled vocabularies for coding data [2].	Produces heterogeneous, non-interoperable data that is difficult to query, analyze, or compare across maps.
Data Storage & Structure	Reliance on rigid, flat data tables (e.g., spreadsheets) with a fixed schema [2].	Poorly captures complex, interconnected relationships in toxicological data (e.g., chemical, outcome, study model), limiting analytical depth.
Critical Appraisal	Applying inappropriate risk-of-bias tools designed for clinical studies to environmental health or toxicological research [54].	Generates misleading quality scores that misrepresent the reliability of the underlying evidence for chemical risk assessment.

Framework for Bias Risk Assessment and Mitigation

Bias in SEMs can stem from the primary research being mapped or can be introduced during the mapping process itself. A structured framework is essential for identification and mitigation.

Table 2: Bias Risks in Evidence Mapping and Corresponding Mitigation Protocols

Bias Type	Definition & Source	Mitigation Protocol
Selection Bias	Arises from non-comprehensive searches or inconsistent screening, leading to a non-representative set of studies [6].	Protocol:1. Pre-publish a search protocol documenting all databases, search strings, and grey literature sources [6].2. Implement pilot screening rounds with multiple reviewers to calibrate application of PECO criteria. Achieve a Kappa statistic >0.8 before proceeding.3. Mandate dual-independent screening for all records, with conflicts resolved by a third reviewer.
Data Extraction Bias	Inconsistent or subjective extraction of key study metadata and results [6].	Protocol:1. Develop and pilot a detailed extraction codebook with explicit definitions for every field.2. Use standardized, controlled vocabularies and ontologies (e.g., MeSH, ChEBI) for coding key concepts like chemicals and outcomes [2].3. Perform dual-independent extraction on a minimum 10% random sample of included studies, with reconciliation of discrepancies.
Confirmation Bias	The unconscious tendency to search for, extract, or interpret data in a way that confirms pre-existing beliefs about a chemical's risk.	Protocol:1. Frame broad, neutral research questions during problem formulation, avoiding leading language [54].2. Blind reviewers to study authors, journals, and funding sources during screening and extraction where feasible.3. Involve a multidisciplinary team with diverse expertise in the review process to challenge assumptions.

Systematic Bias Mitigation Framework

Application Note: Implementing a Knowledge Graph Architecture to Resolve Inconsistency

A primary technical inconsistency is the use of rigid, flat data tables (e.g., spreadsheets) to store complex evidence. A knowledge graph offers a superior, schemaless data model where entities (e.g., Chemical, Study, Outcome) are represented as nodes, and their relationships (e.g., "investigates," "causes") are explicit edges [2]. This model directly addresses heterogeneity and interconnectivity.

Protocol for Knowledge Graph-Based Evidence Mapping:

• Entity & Relationship Definition: Define core entity types (Chemical, Study, Assay, Endpoint, Species) and their allowable relationships using a formal ontology (e.g., integrating the ToxO ontology).
• Graph Database Implementation: Utilize a graph database platform (e.g., Neo4j). The schema is applied "on-read" rather than "on-write," allowing for flexible addition of new entity or relationship types as the evidence base evolves [2].
• Data Ingestion Pipeline: Create a semi-automated pipeline where extracted data is transformed into node and edge records. Coding with controlled vocabularies is critical at this stage.
• Querying and Exploration: End-users (e.g., risk assessors) can perform complex queries (e.g., "Return all in vitro studies investigating endocrine disruption for chemicals with production volume >1000 tons/year") that are impossible with flat tables.

Knowledge Graph vs. Flat Table Data Structure

Experimental Protocol: Quantitative Risk Assessment Integration with SEMs

SEMs are a precursor to quantitative risk assessment (QRA), which quantifies population health impact (e.g., attributable disease cases) [25]. This protocol details how to use a completed, high-quality SEM to inform a QRA.

Title: Protocol for Leveraging a Systematic Evidence Map to Parameterize a Quantitative Risk Assessment for a Chemical.

Objective: To systematically identify and extract dose-response data and study quality information from an SEM to inform the hazard identification and dose-response modeling steps of a QRA.

Materials:

• A completed SEM database (preferably graph-based) for the target chemical class.
• QRA software (e.g., RISKCURVES for consequence modeling [55]) or statistical software (e.g., R, Python).
• Access to population exposure data relevant to the assessment scenario.

Procedure:

• Query the SEM for Hazard Identification:

  • Execute a query to extract all studies reporting on a predefined set of priority health outcomes (e.g., hepatotoxicity, developmental neurotoxicity).
  • Filter results by critical appraisal scores stored within the SEM to create a candidate list of higher-reliability studies [54].

• Dose-Response Data Extraction:

  • For the candidate studies, use the SEM to extract or link to detailed data on exposure levels (dose), response metrics (e.g., % incidence, effect size), study population (species, strain), and exposure duration.
  • Coding is crucial: Ensure doses are converted to a common unit (e.g., mg/kg/day), and responses are standardized where possible.

• Data Synthesis for QRA Input:

  • Apply benchmark dose (BMD) modeling or other meta-analytic techniques to the extracted dose-response data to generate a potency estimate (e.g., a BMDL10 - the lower confidence limit of the benchmark dose for a 10% response).
  • This derived value, along with its uncertainty, becomes a key input for the QRA's risk characterization step [25].

• Uncertainty Analysis:

  • Propagate uncertainties from the evidence mapping (e.g., heterogeneity in study quality, inconsistency in endpoints) and dose-response modeling into the final QRA output using probabilistic methods (e.g., Monte Carlo simulation).

Table 3: Data Flow from SEM to QRA Input

QRA Step	Required Input	SEM Query & Extraction Action
Hazard Identification	List of adverse outcomes linked to the chemical.	Query: MATCH (c:Chemical)-[:causes]->(o:Outcome) RETURN o.name
Dose-Response Assessment	Point of departure (e.g., BMDL10) for critical effect.	Extract dose & response data from high-quality studies; perform meta-analysis/BMD modeling.
Exposure Assessment	Contextual data on use, release, and exposure pathways.	Extract data from "Use" and "Environmental Fate" study nodes within the SEM.
Risk Characterization	Integrated analysis of exposure and dose-response.	Use synthesized inputs from above steps to calculate risk metrics (e.g., Hazard Quotient, MOE).

Evidence Map to Quantitative Risk Assessment Workflow

The Scientist's Toolkit: Essential Reagents for Rigorous Evidence Mapping

Table 4: Research Reagent Solutions for Evidence Mapping

Item/Tool	Function in Evidence Mapping	Key Specification/Note
Protocol Registry (e.g., PROSPERO, Open Science Framework)	To pre-register the SEM protocol, detailing PECO criteria, search strategy, and analysis plan. Mitigates bias and duplication of effort [6].	Must be used before commencing the literature search.
Bibliographic Software (e.g., CADIMA, Rayyan, Covidence)	To manage the import, deduplication, and blinded screening of thousands of search records efficiently.	Should support dual-independent screening with conflict resolution.
Controlled Vocabularies & Ontologies (e.g., MeSH, ChEBI, ToxO)	To provide standardized terms for coding chemicals, outcomes, and study designs, ensuring consistency and interoperability [2].	Essential for enabling complex queries and data integration across maps.
Graph Database Platform (e.g., Neo4j, Amazon Neptune)	To store and query the evidence map as a knowledge graph, capturing complex relationships beyond the capability of spreadsheets [2].	The schemaless, on-read structure accommodates evolving evidence.
Critical Appraisal Tool (e.g., OHAT Risk of Bias, SciRAP)	To formally assess the reliability (internal validity) of individual studies included in the map, based on factors internal to study design [6] [54].	Must be tailored to environmental health/toxicology studies, not clinical trials.
Color Palette Tool (e.g., ColorBrewer, Viz Palette)	To select accessible, colorblind-safe palettes for visualizing map results (e.g., evidence clusters, heatmaps) [56] [57].	Must comply with WCAG 2.1 AA contrast guidelines (≥4.5:1 for normal text) [58] [59].

Optimizing Data Storage with Knowledge Graphs and Ontologies for Complex Data

The field of chemical risk management and drug development faces a critical data challenge: information is locked in vast, disparate repositories ranging from unstructured accident reports and scientific literature to structured experimental databases. Systematic evidence mapping (SEM) has emerged as a vital methodology for navigating this complex landscape, providing a comprehensive overview of broad evidence bases to inform decision-making and prioritize further research [6]. However, the traditional database structures used to support SEMs often struggle to represent and interconnect the multidimensional, heterogeneous data inherent to chemical safety and pharmacology.

Knowledge graphs (KGs) and their foundational ontologies present a transformative solution for optimizing data storage in this context. A knowledge graph is a structured knowledge representation that stores information as entities (nodes) and the relationships between them (edges) [60]. This structure is particularly adept at modeling complex systems—such as the chain of events leading to a chemical accident or the interconnected pathways of drug efficacy and toxicity—allowing for sophisticated querying and inference [61]. Ontologies provide the essential semantic framework for these graphs, defining the concepts, attributes, and relationships within a domain (e.g., "Chemical," "hasProperty," "causesEffect") to ensure consistent interpretation and interoperability across data sources [62].

Framed within a thesis on systematic evidence mapping for chemical risk management, this article posits that the integration of domain-specific ontologies and KGs directly addresses core limitations of conventional data storage. This approach transforms SEMs from static databases into dynamic, queryable networks. It enables the systematic encoding of not just study metadata but the rich, relational knowledge within the evidence—linking chemical structures to their properties, experimental outcomes, hazard scenarios, and epidemiological findings. This evolution supports more transparent, efficient, and insightful evidence-based chemical risk assessment and drug discovery [6] [54].

Foundational Elements: Ontologies for Chemical and Risk Data

Ontologies serve as the critical blueprint for building meaningful and interoperable knowledge graphs. They move beyond simple data schemas by providing a formal, logic-based representation of domain knowledge, enabling both humans and machines to share a common understanding of concepts and their relationships.

In chemical risk sciences, ontologies standardize the representation of complex entities. For instance, the OntoSpecies ontology is designed as a comprehensive semantic database for chemical species [62]. It integrates diverse data by defining classes for identifiers (e.g., CAS number, InChIKey), chemical properties (e.g., molecular weight, boiling point), classifications (e.g., role in application like "solvent"), and spectral data. Crucially, it includes provenance metadata, ensuring the traceability and reliability of each data point—a fundamental requirement for evidence-based risk assessment [62].

For process safety, the HAZOP Hazard Scenario Ontology (HHSO) demonstrates how ontology design can model dynamic risk. Moving beyond static entity lists, HHSO is built around the concept of a "Hazard Scenario," linking deviations, causes, consequences, and safeguards in a way that explicitly represents potential hazard propagation paths through a system [63]. This allows the knowledge graph to answer complex questions about event chains and vulnerabilities.

Table: Core Ontology Classes for Chemical Risk Management

Ontology Name	Primary Domain	Key Conceptual Classes/Entities	Primary Function
OntoSpecies [62]	Chemical Identity & Properties	ChemicalSpecies, Identifier, MolecularProperty, SpectralData	Unifies chemical identifiers and properties from multiple sources for reliable querying.
HHSO (Hazard Scenario) [63]	Process Safety & Hazard Analysis	HazardScenario, Deviation, Cause, Consequence, Safeguard, ProcessUnit	Models the logical progression of hazardous events for risk pathway analysis.
Exposure Assessment	Human Health Risk [64]	Population, ExposurePathway, ExposureRoute, Dose	Structures data on how, where, and to whom chemical exposure occurs.
Toxicological Outcome	Human Health Risk [64]	AdverseEffect, ModeOfAction, DoseResponse, StudyType	Categorizes health effects and the biological mechanisms linking exposure to outcome.

Protocols for Constructing Domain-Specific Knowledge Graphs

Constructing a high-quality, domain-specific knowledge graph is a multi-stage process involving ontology design, automated knowledge extraction, and data refinement. The following protocols detail methodologies drawn from recent research in chemical safety and biomedicine.

Protocol 1: Semi-Automated KG Construction from Unstructured Text

This protocol outlines the process for building a knowledge graph from unstructured textual reports, such as hazardous chemical accident (HCA) investigations [60].

• Data Preparation & Ontology Definition:

  • Source Collection: Gather the target corpus (e.g., HCA investigation reports, scientific literature).
  • Text Preprocessing: Clean and normalize the text. For multilingual corpora (e.g., Chinese reports), apply word segmentation.
  • Ontology Development: Define the domain ontology using a seven-step method [60]. Identify core entity types (e.g., Chemical, Equipment, Location, PersonnelAction) and relation types (e.g., involvesChemical, causedBy, occurredAt). This creates the schema layer (TBox) of the KG.

• Knowledge Extraction with the IRTI Model:

  • Model Application: Process the text corpus using the Interaction Region and Type Information (IRTI) deep neural network [60]. This model is specifically designed for the joint task of Named Entity Recognition (NER) and Relation Extraction (RE) from lengthy texts with complex, overlapping entity relationships.
  • Output: The model extracts factual triples in the form (Subject Entity, Relation, Object Entity) from each report, such as (Valve-X-21, hasDeviation, High Pressure).

• Knowledge Standardization & Enhancement:

  • Entity Standardization: Input non-standard entity mentions (e.g., "NaCl," "sodium chloride," "table salt") into a pipeline combining ChatGPT-4 and a Contrastive Learning-based Short Text Clustering (CLSTC) model. This maps variant terms to canonical identifiers (e.g., a PubChem CID) [60].
  • External Knowledge Fusion: Enrich extracted entities by linking them to external authoritative databases (e.g., PubChem, ChEBI) using their standardized identifiers to pull in additional properties and classifications [62].

• Graph Population & Storage:

  • Triple Storage: Load the standardized and enriched triples into a graph database (e.g., Neo4j, Amazon Neptune, or a triplestore like Virtuoso for RDF/OWL).
  • Indexing: Create indexes on key entity properties to enable efficient querying.

Protocol 2: Building an Ontology-Centric KG for Process Safety

This protocol focuses on constructing a KG where the ontology design explicitly models complex event chains, as used in HAZOP-based safety analysis [63].

• Hazard-Centric Ontology Design:

  • Scenario Modeling: Define the ontology around the HazardScenario as a central class. Link it via object properties to Deviation, Cause, Consequence, and Safeguard classes.
  • Pathway Representation: Ensure the ontology can represent sequences, such as Cause -> leadsTo -> Deviation -> resultsIn -> Consequence -> mitigatedBy -> Safeguard.

• Specialized NER for Domain Text:

  • Model Training: Develop or train a Multi-Feature Fusion Named Entity Recognition (MFFNM) model on a manually annotated corpus of domain texts (e.g., HAZOP reports) [63].
  • Feature Engineering: The MFFNM should integrate character-level, lexical (domain dictionary), and semantic (radical features for Chinese) information to accurately identify entities in technical language.

• Relation Assembly & Graph Instantiation:

  • Rule-Based & ML Relation Building: Use a combination of heuristic rules (based on report structure) and trained relation classification models to link the extracted entities according to the ontology's properties.
  • ABox Creation: Instantiate the ontology by creating individual instances (the ABox) from the extracted and linked data, populating the KG with concrete hazard scenarios.

• Application & Query Interface:

  • Develop Query Templates: Create parameterized query templates (e.g., in Cypher or SPARQL) for common questions like "Find all scenarios where a 'high temperature' deviation led to a 'fire' consequence."
  • Natural Language Interface: Implement a layer using a large language model (LLM) to translate natural language questions into structured graph queries, democratizing access to the KG [63].

Diagram: Workflow for Constructing a Knowledge Graph from Unstructured Data [60] [63]

Application in Systematic Evidence Mapping and Chemical Risk Management

Integrating KGs into the SEM workflow revolutionizes how evidence is stored, connected, and analyzed for chemical risk assessment and drug safety evaluation.

• Enhanced Evidence Organization: An SEM built on a KG framework moves beyond a flat table of studies. Each study becomes a node that can be linked to nodes representing the tested chemicals (with properties from OntoSpecies), specific adverse outcomes, exposed populations, and experimental models. This allows for multi-faceted filtering and grouping that reflects the complexity of the underlying biology and chemistry [6] [62].

• Identification of Evidence Glands and Trends: Graph analytics can traverse connections to identify clusters of research on certain chemical classes or outcomes, and more importantly, reveal clear gaps where few or no connections exist (e.g., a widely used chemical with no chronic toxicity data for a susceptible sub-population). This provides a powerful, visual evidence-based tool for research prioritization [6].

• Supporting Quantitative Risk Assessment: The KG can directly feed into the traditional risk assessment paradigm [64] [54]. For Hazard Identification, the graph can aggregate all studies linked to a chemical and its metabolites, categorizing evidence by AdverseEffect and ModeOfAction. For Dose-Response analysis, it can help select key studies by facilitating comparison based on study quality, relevance of exposure route, and appropriateness of the model system—factors that are explicitly modeled as node properties and relationships [54].

• Drug Discovery and Safety: In pharmaceutical research, KGs integrate data from target biology, compound screenings, pharmacokinetics, and clinical trial outcomes [61] [65]. This enables the prediction of drug repurposing opportunities, the identification of potential adverse effect pathways before clinical stages, and ensures compliance with identification standards like IDMP for pharmacovigilance [65].

Table: Performance of Knowledge Extraction Models for Chemical Risk KGs

Model Name	Application Domain	Key Metric	Reported Performance	Primary Advantage
IRTI (Interaction Region and Type Information) [60]	Hazardous Chemical Accident Reports	Relation Extraction F1-Score	High Performance (Exact values not provided in abstract)	Handles long texts with complex, overlapping entity relationships.
MFFNM (Multi-Feature Fusion NER Model) [63]	Chinese HAZOP Reports	NER F1-Score	93.03%	Integrates character, word, and radical features for domain-specific text.
CLSTC (Contrastive Learning-based Short Text Clustering) [60]	Entity Standardization	Clustering Accuracy	High Performance (Exact values not provided in abstract)	Effective for standardizing diverse textual entity mentions to canonical forms.

Diagram: Knowledge Graph-Enhanced Systematic Evidence Mapping Workflow [6] [54]

Table: Key Resources for Knowledge Graph Construction in Chemical Sciences

Resource Name	Type	Primary Function in KG Workflow	Key Features / Use Case
OntoSpecies Ontology [62]	Domain Ontology	Provides the schema for representing chemical species, their identifiers, properties, and classifications.	Comprehensive, includes provenance; core for any chemical-centric KG.
PubChem / ChEBI [62]	Reference Database	Serves as a canonical source for chemical identifier mapping and property enrichment.	Essential for entity standardization and data fusion.
IRTI or MFFNM Models [60] [63]	Deep Learning Model	Performs the core task of extracting entities and relations from unstructured domain text (e.g., reports, literature).	Specialized for technical language and complex textual relationships.
Large Language Model (e.g., GPT-4) [60]	NLP Tool	Assists in entity standardization, query translation (natural language to graph query), and potentially summarization.	Enhances automation and usability of the KG system.
Graph Database (e.g., Neo4j, Amazon Neptune)	Storage Infrastructure	Stores and allows for efficient traversal and querying of the graph's nodes and edges.	Supports Cypher query language; optimized for network operations.
Triplestore (e.g., Virtuoso)	Storage Infrastructure	Stores RDF/OWL-based knowledge graphs; enables SPARQL querying and semantic reasoning.	Required for ontologies with complex logical constraints (OWL).
SPARQL / Cypher	Query Language	The language used to interrogate the knowledge graph to retrieve specific information and patterns.	SPARQL for RDF graphs; Cypher for property graphs.

Streamlining Workflows with Automation and Specialized Software Applications

In the field of chemical risk management and drug development, evidence-based decision-making is paramount [1]. The growing volume of toxicological and environmental health research presents a significant challenge: efficiently locating, organizing, and evaluating all relevant data to inform regulatory and safety assessments [2]. Traditional systematic reviews (SRs), while robust, are resource-intensive and focused on answering narrowly defined questions, which can be ill-suited to the broad, interconnected information needs of chemical policy workflows [1].

Systematic evidence mapping (SEM) has emerged as a critical precursor and enabler for efficient workflow automation within this domain. An SEM is a queryable database of systematically gathered research that characterizes the broad landscape of available evidence [1] [2]. It does not perform a full synthesis but instead organizes metadata and key findings, allowing researchers to identify evidence clusters, gaps, and trends. This structured, digital evidence base is the essential foundation upon which specialized software applications and automation protocols can be built to streamline the entire risk assessment pipeline—from literature surveillance and data extraction to hazard characterization and reporting.

This document provides detailed Application Notes and Protocols for implementing automated workflows centered on systematic evidence mapping, designed for researchers, scientists, and professionals engaged in chemical risk management and drug development.

Application Notes: Integrating Automation into Evidence Mapping

The Paradigm Shift: From Manual Curation to Hyperautomation

The manual management of scientific evidence is a bottleneck characterized by lost productivity, delayed decisions, and potential compliance risks [66]. The strategic integration of multiple technologies, known as hyperautomation, is transitioning from a trend to a necessity [67]. In an evidence mapping context, this involves the coordinated use of:

• AI and Machine Learning (ML): For intelligent literature screening, data extraction, and trend prediction.
• Robotic Process Automation (RPA): For automating repetitive tasks across different software, such as logging into databases, downloading studies, and populating standardized forms.
• Process Intelligence: To analyze and optimize the evidence mapping workflow itself.

Gartner reports that 90% of large organizations are now prioritizing such hyperautomation initiatives [67]. For research institutions, this translates to systems that can automatically update evidence maps with new publications, flag studies for critical endpoints, and route findings to relevant risk assessment teams.

The Role of No-Code/Low-Code Platforms

A significant barrier to workflow automation has been the dependency on IT specialists. The rise of no-code and low-code platforms democratizes automation, enabling subject matter experts (e.g., toxicologists, risk assessors) to design and modify workflows [67]. These platforms feature intuitive visual builders and drag-and-drop interfaces. Gartner predicts that by 2025, 70% of new enterprise applications will use these technologies, a substantial increase from less than 25% in 2020 [67]. In a research setting, a scientist can use a no-code tool to create an automated workflow that triggers a systematic data extraction protocol whenever a new study on a specific chemical is added to the evidence map, significantly accelerating the review cycle.

Foundational Protocol: Chemical Risk Management Standard

Any automation must be built upon a foundation of standardized, safe operational procedures. The Chemical Risk Management Standard provides this foundation, outlining the mandatory framework for handling hazardous chemicals to protect human health and the environment [68]. Automated workflows for evidence mapping and risk assessment must be designed to comply with and reinforce these standards. For instance, an automated system can ensure that all research data pertaining to a highly hazardous chemical is automatically linked to its corresponding Safe Methods of Use (SMOUs) document, which details specific handling, storage, and disposal procedures [68].

Table 1: Comparison of Systematic Review (SR) and Systematic Evidence Map (SEM) Workflows

Feature	Systematic Review (SR)	Systematic Evidence Map (SEM)	Automation Potential
Primary Goal	Answer a specific, narrow question via synthesis.	Characterize the breadth of available evidence for a broader topic [1].	SEM's broader data collection is highly amenable to automated literature surveillance.
Resource Intensity	Very High (time, personnel, cost).	Moderate to High (focus is on cataloging, not full synthesis).	Automation can significantly reduce the moderate resource burden of SEM.
Output	Qualitative/quantitative summary with meta-analysis.	Queryable database or interactive visualization [2].	The digital, structured output is the ideal input for further automated analysis tools.
Decision-Making Role	Provides a definitive answer for a specific risk parameter.	Informs priority-setting and scoping for future SRs or primary research [1] [2].	AI can analyze the SEM database to automatically recommend priority research questions.

Detailed Experimental Protocols

Protocol 1: Constructing a Systematic Evidence Map for a Chemical Class

Objective: To create a queryable database of all published literature on the ecotoxicological effects of a specified class of per- and polyfluoroalkyl substances (PFAS).

Materials:

• Information Sources: Bibliographic databases (PubMed, Scopus, Web of Science), regulatory agency reports (EPA, ECHA), specialized toxicology databases.
• Software: Reference management software (e.g., EndNote, Zotero), systematic review automation tools (e.g., Rayyan, ASReview), data extraction and database management platforms.
• Structured Data Extraction Sheet: Pre-defined fields (e.g., chemical ID, study type, organism, endpoint, exposure data, result).

Methodology:

• Protocol Development & Registration:
  • Define the research question and inclusion/exclusion criteria (PECO: Population, Exposure, Comparator, Outcome).
  • Publish the protocol on a public registry (e.g., PROSPERO, Open Science Framework) to ensure transparency.

• Search Strategy Execution (Automated):

  • Develop a comprehensive search string for each database.
  • Utilize scripting (e.g., Python with APIs) or RPA tools to execute searches across all platforms simultaneously and aggregate results into a single library, removing duplicates using automated deduplication algorithms.

• Screening Process (AI-Assisted):

  • Title/Abstract Screening: Use an ML-based tool (e.g., ASReview) which actively learns from your initial screening decisions to prioritize the most relevant records, reducing screening workload by up to 90%.
  • Full-Text Screening: Conduct manually with decisions recorded in a workflow-managed platform.

• Data Extraction & Coding (Semi-Automated):

  • Use a standardized electronic data extraction form.
  • Employ natural language processing (NLP) models to pre-fill fields from the full text (e.g., chemical names, numerical results) for researcher verification.
  • Code extracted data using a controlled vocabulary or ontology (e.g., ECOTOX ontology) to ensure consistency and queryability.

• Database Development & Quality Control:

  • Structure the extracted and coded data into a relational database or, preferably, a knowledge graph (see Protocol 2).
  • Implement automated consistency checks (e.g., range checks for numerical values, validation against chemical identifiers).
  • Perform manual verification on a random sample of entries.

• Visualization & Reporting:

  • Generate automated evidence atlases (heat maps) showing volume of research by chemical, endpoint, and species.
  • The final output is an interactive, queryable SEM database that identifies which PFAS compounds and toxicological endpoints are well-studied and where critical gaps exist [2].

Protocol 2: Implementing a Knowledge Graph for Evidence Interconnection

Objective: To move beyond a flat SEM database and construct a dynamic knowledge graph that semantically links chemicals, studies, toxicological outcomes, and regulatory actions.

Rationale: Traditional flat databases struggle with the highly connected and heterogeneous nature of environmental health data [2]. A knowledge graph uses a graph-based data model (nodes, edges, properties) to naturally represent these relationships, enabling more powerful queries and AI-driven discovery [2].

Materials:

• Graph Database: Neo4j, Amazon Neptune, or a graph-enabled triple store (e.g., Blazegraph).
• Ontologies: Publicly available toxicology ontologies (e.g., ECOTOX, ChEBI, Uberon) to provide standardized terminology.
• Data: Output from Protocol 1 (the SEM).

Methodology:

• Schema Design (On-Read, Schemaless Approach):
  • Leverage the flexibility of graph databases by adopting a "schemaless" or "schema-on-read" approach, which is better suited to evolving evidence than rigid, pre-defined tables [2].
  • Define core node types: Chemical, Study, Assay, Endpoint, Organism.
  • Define core relationship types: CHEMICAL_TESTED_IN -> STUDY, STUDY_REPORTED -> ENDPOINT, ENDPOINT_MEASURED_USING -> ASSAY.

• Data Transformation & Ingestion:

  • Map the coded data from the SEM to the graph model. For example, a row of data becomes a Study node connected to a Chemical node, which is connected to an Endpoint node classified using an ontology term.
  • Use automated scripts to convert the structured SEM data into graph queries (e.g., Cypher for Neo4j) for batch ingestion.

• Enrichment with External Data:

  • Automatically link Chemical nodes to public databases (via APIs) to pull in properties (molecular weight, structure) and regulatory status (e.g., TSCA listings from EPA [69]).
  • Link Study nodes to publication databases to fetch citations and author networks.

• Querying and Analysis:

  • Perform complex queries not possible in flat databases. Example: "Find all studies where any chemical structurally similar to Chemical X caused Outcome Y in mammalian models, and show related regulatory risk evaluations."
  • Apply graph analytics algorithms (e.g., community detection) to identify clusters of co-occurring chemicals and endpoints, revealing hidden patterns in the evidence base.

Diagram 1: Knowledge Graph Fragment for a PFAS Study. This graph semantically links a chemical, a specific study, the toxicological endpoint found, the assay used, the test organism, and related regulatory information.

Table 2: Quantified Benefits of Workflow Automation in Research

Metric	Manual Process Benchmark	With Integrated Automation	Data Source / Rationale
Literature Screening Time	100% (Baseline)	Reduced by 50-90% [67]	Use of Active Learning AI tools prioritizes relevant records.
Data Extraction Error Rate	Subjective; Higher	Minimized via NLP pre-fill & validation rules	Automated checks ensure consistency and reduce manual entry mistakes.
Evidence Base Update Cycle	Months (Ad-hoc projects)	Continuous (Ongoing surveillance)	Automated search alerts and ingestion pipelines keep SEMs current.
Stakeholder Access to Evidence	Limited to report authors	Democratized via queryable databases/APIs	No-code front-ends allow non-specialists to explore evidence [67].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Software & Digital "Reagents" for Automated Evidence Workflows

Item Name	Category	Function in Workflow	Protocol Application
Cflow / Similar No-code Platform [67]	Workflow Automation Builder	Enables researchers to visually design, automate, and manage multi-step evidence review and approval processes without coding.	Protocol 1, Step 3-5: Managing the screening and data extraction pipeline, routing tasks, and ensuring QC steps are completed.
Rayyan / ASReview	AI-Assisted Screening Tool	Uses machine learning to expedite title/abstract screening by learning from user decisions and prioritizing likely relevant studies.	Protocol 1, Step 3: Dramatically reduces the manual screening burden in the initial phase of evidence mapping.
Neo4j	Graph Database	Provides a native platform to store, query, and analyze interconnected data as a knowledge graph, revealing complex relationships.	Protocol 2: The core technology for implementing the interconnected knowledge graph of chemical evidence.
EPA TSCA Risk Evaluation Database [69]	Regulatory Data Source	A structured, public source of regulatory hazard and risk assessments for chemicals. Used to enrich and validate evidence maps.	Protocol 2, Step 3: Automated scripts can link chemicals in the knowledge graph to their official TSCA risk evaluation status and conclusions.
Chemical Safety SMOUs [68]	Standard Operating Procedure	Safe Methods of Use documents are critical for lab safety. Automated systems can link chemical data in evidence maps to relevant handling protocols.	Foundation for all wet-lab work informed by evidence mapping; can be integrated into digital lab notebooks.

Diagram 2: Automated Systematic Evidence Mapping Workflow. This flowchart outlines the integrated, semi-automated process for creating and maintaining a dynamic evidence base, featuring feedback loops for continuous improvement.

The integration of systematic evidence mapping with specialized automation software and knowledge graph technology represents a transformative shift in chemical risk management research. This approach moves beyond simply speeding up old tasks; it reimagines the workflow to create a living, interconnected evidence ecosystem. The protocols outlined herein provide a roadmap for research organizations to implement these strategies, leading to more transparent, reproducible, and responsive risk assessment processes. As these automated, evidence-centric workflows mature, they will form the backbone of next-generation, predictive toxicology and safety assessment frameworks, ultimately enhancing the protection of human health and the environment.

Ensuring Transparency and Reproducibility in Mapping Exercises

The field of chemical risk management faces a dual challenge: an expanding universe of chemicals requiring assessment and increasing demand for transparent, evidence-based decision-making. Traditional narrative reviews are susceptible to bias and lack reproducibility, while full systematic reviews, though robust, are resource-intensive and narrow in scope [6]. This creates a critical gap in efficiently synthesizing broad evidence bases for regulatory programs like EU REACH and US TSCA [6].

Systematic Evidence Mapping (SEM) emerges as a pivotal methodology to bridge this gap. An SEM is defined as a database of systematically gathered research that characterizes broad features of an evidence base without performing a full quantitative synthesis [6]. Its core value lies in providing a comprehensive, queryable overview that supports priority-setting, identifies evidence clusters and gaps, and guides targeted systematic reviews or primary research [6]. The necessity for such an approach is underscored by documented failures in transparency, where a lack of detailed methodological documentation has prevented the replication of pivotal risk assessments, such as for formaldehyde [70].

This document provides detailed application notes and experimental protocols for conducting SEMs with an uncompromising focus on transparency and reproducibility, framed within a thesis on advancing chemical risk management research.

Foundational Principles and Comparative Framework

An SEM is distinguished from a Systematic Review (SR) by its objectives and outputs. An SR aims to answer a specific, narrow question (via a PECO statement) with a synthesized finding, while an SEM aims to systematically catalog and characterize the available evidence for a broader field of inquiry [6].

Table 1: Comparison of Systematic Review (SR) and Systematic Evidence Map (SEM) Approaches

Feature	Systematic Review (SR)	Systematic Evidence Map (SEM)
Primary Objective	Answer a focused question with a synthesized conclusion.	Catalog and characterize the breadth of evidence for a defined field.
Research Question	Narrow, specific (PECO framework).	Broad, scoping.
Evidence Synthesis	Quantitative and/or qualitative synthesis (meta-analysis).	No synthesis; characterization and visualization of evidence patterns.
Output	Effect estimate, certainty rating, definitive conclusions.	Searchable database, evidence inventory, gap analysis, visual maps.
Resource Intensity	High (12-24 months).	Moderate to High (6-18 months).
Key Utility	Directly informs risk values and decisions.	Informs research prioritization, identifies needs for SR, surveils evidence base.

The guiding principles for a transparent and reproducible SEM are:

• Protocol-First: A detailed, publicly registered protocol pre-defines all methods.
• Comprehensive Documentation: Every decision, deviation, and analytical step is recorded.
• Structured Data Capture: Data is extracted into predefined, coded fields for objectivity.
• Replicable Workflow: The process from search to final map can be followed by an independent team.
• Accessible Outputs: Results are shared in open, interoperable formats.

Experimental Protocols for Systematic Evidence Mapping

Protocol Registration and Development

Objective: To pre-specify and publicly commit to the SEM methodology, minimizing subjective post-hoc decisions. Procedure:

• Develop Protocol: Draft a document containing: Rationale; Objectives; Stakeholder engagement plan; Search strategy (databases, date limits, syntax); Eligibility criteria (PECO components); Study selection process (screening phases, conflict resolution); Data extraction fields and codebook; Critical appraisal plan; Strategy for evidence characterization and mapping; Data management and sharing plan.
• Register Protocol: Submit the finalized protocol to a public registry such as the Open Science Framework (OSF) or PROSPERO (for health-related maps). The time-stamped, version-controlled protocol is the foundational document for reproducibility.

Evidence Identification and Screening

Objective: To identify all potentially relevant records through a comprehensive, documented search and filter them against pre-defined eligibility criteria. Procedure:

• Search Execution: Execute the registered search strings across multiple bibliographic databases (e.g., PubMed, Scopus, Web of Science, TOXLINE) and grey literature sources. Record the exact date of search and number of records retrieved per source.
• Reference Management: Import all records into a systematic review management tool (e.g., Rayyan, Covidence, DistillerSR) or a reproducible workflow using bibliographic software (e.g., Zotero, EndNote) and scripting (e.g., R, Python).
• Deduplication: Apply a documented algorithm for removing duplicate records and report the count removed.
• Screening:
  • Title/Abstract Screening: Two independent reviewers screen each record against eligibility criteria. Pre-test screening rules on a sample (e.g., 100 records) to calibrate. Document reasons for exclusion. Calculate and report inter-rater reliability (e.g., Cohen's Kappa).
  • Full-Text Screening: Retrieve and screen the full text of all potentially eligible records. Two independent reviewers assess eligibility, resolving conflicts through consensus or third-party adjudication. Maintain a log of excluded studies with specific reasons.

Diagram 1: Systematic Evidence Mapping Workflow with Key Checkpoints (Max Width: 760px)

Data Extraction and Critical Appraisal

Objective: To consistently capture relevant study characteristics and assess the internal validity (risk of bias) of included studies. Procedure:

• Develop Codebook: Create a detailed data extraction codebook defining each field (variable), its allowed values (coded list), and instructions for coders.
• Pilot Extraction: Test the codebook on a sample of 5-10 studies, refine it based on coder feedback, and finalize.
• Dual Extraction: Two trained reviewers independently extract data from each included study into the structured codebook (implemented via a form in DistillerSR, a REDCap survey, or a shared spreadsheet with locked cells).
• Consensus & Adjudication: Resolve discrepancies between extractors through discussion. Document unresolved issues and final decisions.
• Critical Appraisal: Apply a domain-based risk of bias tool appropriate to the study designs present (e.g., OHAT, ROBINS-I, SYRCLE for animal studies). Perform dual independent assessment.

Table 2: Evidence Characterization and Data Extraction Fields (Example)

Category	Field Name	Description/Values	Purpose
Study ID	Citation	Author, Year, Journal, DOI	Unique identifier and reference.
PECO	Population	Species, Strain, Sex, Age/Life Stage	Characterizes biological test system.
	Exposure	Chemical, CASRN, Dose/Conc., Route, Duration	Characterizes the intervention.
	Comparator	Control group type (e.g., vehicle, sham).	Basis for comparison.
	Outcome	Endpoint measured (e.g., liver weight, gene expression).	Maps evidence by health effect.
Study Design	Study Type	In vivo, In vitro, Epidemiological, etc.	Filters and characterizes evidence.
	Guideline	If compliant with OECD, EPA, etc.	Indicates standardization.
Results	Direction of Effect	Increase, Decrease, No Effect, Not Reported.	Foundational for semantic analysis [71].
	Statistical Significance	Reported p-value or confidence interval.	Characterizes result strength.
Appraisal	Risk of Bias	Final judgment per domain (Low/Some/High Concern).	Informs confidence in evidence base.

Evidence Synthesis, Mapping, and Visualization

Objective: To organize the extracted data into a queryable database and create visual representations that reveal the structure of the evidence base. Procedure:

• Database Creation: Clean and structure the extracted data into a relational database (e.g., SQLite, PostgreSQL) or a flat file with consistent formatting (e.g., CSV). Assign a persistent identifier (e.g., DOI) to the final dataset.
• Evidence Characterization: Generate descriptive statistics summarizing the evidence base (e.g., number of studies per chemical, per outcome, per study type, over time).
• Gap Analysis: Systematically identify understudied areas (e.g., chemicals with few studies, missing outcomes for a key chemical class, lack of chronic low-dose data).
• Generate Evidence Maps:
  • Heat Maps: Use matrices where rows are chemicals and columns are outcomes, with cells colored by the volume or quality of evidence.
  • Interactive Visualizations: Develop web-based visualizations (e.g., using R Shiny, Plotly) allowing users to filter by PECO elements, study type, or risk of bias.
  • Network Diagrams: Illustrate connections between chemicals, molecular targets, and adverse outcomes.

Color and Accessibility Protocol for Visualizations: Adherence to visual accessibility standards is a cornerstone of transparent communication [72].

• Contrast Ratios: All text and key graphical elements must meet WCAG 2.1 AA standards: a minimum contrast ratio of 4.5:1 for normal text and 3:1 for large text and graphics [73] [72]. Use online checkers (e.g., WebAIM) for verification.
• Color Palette Selection:
  • Sequential Data (e.g., dose gradient): Use a single-hue gradient with perceptually uniform lightness steps (e.g., viridis, plasma). Avoid rainbow ('jet') palettes as they are not perceptually linear and can misrepresent data [74].
  • Categorical Data (e.g., study types): Use a colorblind-friendly palette with distinct hues (e.g., ColorBrewer Set2, Okabe-Ito). Limit to a maximum of 7-8 colors [75] [76].
• Redundant Encoding: Do not rely on color alone. Differentiate elements using shapes, patterns, or direct labels [73] [76].

Advanced Protocol: Semantic Extraction for Scalability and Directionality

Objective: To implement a semi-automated, transparent text-mining approach to scale evidence identification and, crucially, extract the direction of effect (supporting, refuting, or neutral) from study abstracts [71]. This addresses a major limitation of "black-box" machine learning models used in risk assessment.

Procedure:

• Define Semantic Rules: For target outcomes (e.g., "cell proliferation", "cell death"), create knowledge-based rules using Unified Medical Language System (UMLS) synonyms and Natural Language Processing (NLP) to handle coordinated ellipses (e.g., identifying "cell death and proliferation") [71].
• Implement Claim Framework: Apply the Claim Framework to sentences containing target outcomes to classify them as "Explicit Claims" (entity changed + changer + direction) or "Observations" [71].
• Directionality Classification: Use a deep learning model (e.g., a transformer-based classifier) trained on a manually annotated corpus to classify the directionality of each claim into: Supporting (increase), Neutral (change reported, direction unknown), or Refuting (decrease). Explicitly identify negation (e.g., "did not increase") [71].
• Human-in-the-Loop Validation: A subset of the algorithm's classifications (e.g., 20%) is manually verified by a domain expert. Performance metrics (precision, recall) are calculated and reported. The algorithm is iteratively refined based on errors.
• Visualize Spectrum of Evidence: Generate "waffle plots" or similar visualizations for each chemical-outcome pair, showing the proportion of abstracts reporting supporting, neutral, and refuting evidence [71].

Diagram 2: Transparent Semantic Evidence Extraction Workflow (Max Width: 760px)

Table 3: Performance Metrics for Transparent Semantic Extraction (Hypothetical Data)

Target Outcome	Precision (Supporting Evidence)	Recall (Supporting Evidence)	Human-Expert Agreement (Kappa)	Key Challenge Identified
Cell Proliferation	0.89	0.82	0.75	Distinguishing "no significant increase" from neutral reports.
Oxidative Stress	0.78	0.91	0.68	High synonym variability (ROS, lipid peroxidation, etc.).
Apoptosis	0.93	0.87	0.81	Accurate detection of negated refuting claims.

Table 4: Research Reagent Solutions for Transparent and Reproducible SEM

Tool Category	Specific Tool/Resource	Function in Transparency/Reproducibility
Protocol Registration	Open Science Framework (OSF), PROSPERO	Provides time-stamped, version-controlled public record of planned methods.
Search Management	PubMed, Scopus, Web of Science, TOXLINE	Reproducible search syntax can be saved, shared, and re-executed.
Screening & Extraction	Rayyan, Covidence, DistillerSR, REDCap	Audit trails document screening decisions and data extraction changes. Supports dual review workflows.
Data Analysis & Visualization	R (with metafor, ggplot2, shiny), Python (with pandas, plotly, spaCy)	Scripted analyses ensure complete reproducibility. Code sharing allows direct replication.
Semantic/NLP Tools	Unified Medical Language System (UMLS), spaCy, AllenNLP	Provides standardized vocabulary and open-source frameworks for reproducible text mining [71].
Color Accessibility	ColorBrewer 2.0, WebAIM Contrast Checker, Coblis Simulator	Ensures visual outputs are interpretable by all users, including those with color vision deficiencies [73] [75] [74].
Data & Code Repository	GitHub, GitLab, OSF, Zenodo	Permanent, citable storage for final datasets, analysis code, and extraction codebooks.
Reporting Guideline	PRISMA-ScR (Preferred Reporting Items for Systematic reviews and meta-Analyses extension for Scoping Reviews)	Provides a checklist to ensure complete and transparent reporting of the SEM process.

Engaging Stakeholders for Enhanced Usability and Impact in Decision-Making

Within the complex domain of chemical risk management, the challenge for researchers and regulators is not merely a scarcity of data, but an overabundance of fragmented evidence. Traditional systematic reviews (SRs), while rigorous, are often too narrow and resource-intensive to address the broad, interconnected questions posed by modern chemical policy and industrial safety [1]. This gap necessitates a tool that can efficiently organize vast research landscapes to directly inform decision-making. Systematic Evidence Maps (SEMs) emerge as this critical solution, serving as queryable databases that characterize the breadth and depth of available evidence on a given topic, such as the health effects of a class of chemicals or the efficacy of risk mitigation strategies [1] [16].

The true power of an SEM is unlocked not in isolation but through strategic stakeholder engagement. An evidence map's usability and impact are fundamentally contingent on its relevance to the end-users—policymakers, industrial safety managers, and fellow researchers. Engaging these stakeholders throughout the mapping process ensures the final product aligns with real-world informational needs, prioritizes the most decision-critical gaps, and employs visualization formats that facilitate comprehension and action [77]. This article details the integrated methodology and protocols for constructing SEMs in chemical risk management, with a core focus on embedding stakeholder engagement to enhance the utility of the resulting evidence syntheses and their subsequent application in quantitative risk assessment models.

Methodology: Integrating Stakeholder Engagement with Systematic Mapping

The development of a decision-relevant SEM is a multi-phase process that systematically integrates evidence collection with stakeholder input. The following workflow and engagement strategy provide a structured approach.

Systematic Evidence Mapping Workflow

The SEM process adapts the rigor of systematic review to a broader, more descriptive scoping purpose. The key phases are outlined in the table below.

Table 1: Phased Workflow for Conducting a Systematic Evidence Map (SEM)

Phase	Key Activities	Tools & Outputs
1. Scope & Protocol	Define broad research question; develop inclusion/exclusion criteria; pre-register protocol.	PECO/PICO framework; stakeholder workshops.
2. Search & Retrieval	Execute structured, comprehensive search across multiple databases; document search strategy.	PubMed, Web of Science, Scopus; search log.
3. Screening	Screen records (title/abstract, full-text) against criteria; ensure inter-reviewer reliability.	Rayyan, Covidence; PRISMA flow diagram.
4. Data Extraction & Coding	Extract metadata (e.g., chemical, study type, endpoint) into a structured, queryable database.	Custom data extraction forms; Excel, SQL.
5. Critical Appraisal (Optional)	Assess risk of bias when categorizing studies by effect direction or informing future SRs [16].	ROBINS-I, SYRCLE's tool.
6. Synthesis & Visualization	Generate descriptive summaries, heat maps, and network diagrams to illustrate evidence clusters and gaps [16].	R, Python, VOSviewer; interactive web platforms.

A Framework for Stakeholder Engagement

Effective engagement is not a single event but a continuous process built on core principles. The following framework, synthesizing best practices, should guide interactions throughout the SEM lifecycle [77] [78].

Table 2: Core Principles and Activities for Stakeholder Engagement

Engagement Principle	Operational Activities in SEM Context	Project Phase
Diversity & Inclusivity	Identify and recruit stakeholders from academia, industry, regulatory bodies (e.g., EPA), and NGOs to capture multifaceted perspectives [78].	Scoping & Protocol
Listening & Value Creation	Conduct needs-assessment interviews or surveys to shape the SEM's scope, ensuring it addresses stakeholders' core decision problems [77].	Scoping & Protocol
Trust & Transparency	Share draft protocols and preliminary findings for feedback; make search strategies and data fully accessible.	All Phases
Accountability	Document how stakeholder input influenced final decisions (e.g., scope modifications) and report back on outcomes.	Data Synthesis & Reporting
Flexibility & Adaptability	Be prepared to refine search strategies or visualization formats based on stakeholder feedback on preliminary outputs.	Screening & Synthesis

Quantitative Foundations: Data for Mapping and Modeling

The construction of an SEM and the risk models it informs rely on robust quantitative data. Recent analyses provide key insights into emerging research trends and the quantitative factors driving chemical risks.

Table 3: Bibliometric Analysis of Machine Learning in Environmental Chemical Research (1996-2025)

Metric	Finding	Implication for SEM/Stakeholders
Publication Volume	Exponential growth post-2015; over 719 publications in 2024 alone [79].	SEMs must capture this rapidly evolving field; stakeholders need tools to track ML-based evidence.
Leading Countries	China (1130 publications) and the USA (863 publications) are dominant producers [79].	Engagement and collaboration with these research hubs are crucial for comprehensive evidence gathering.
Thematic Clusters	Eight key clusters identified, including ML model development, water quality prediction, and PFAS studies [79].	SEMs can use these clusters to categorize evidence; regulators can identify areas of high research activity.
Research Gap	4:1 bias in keyword frequency toward environmental endpoints over human health endpoints [79].	SEMs can explicitly map this disparity, highlighting a critical gap for funding agencies and researchers.

Table 4: Quantitative Analysis of Risk Factors in Chemical Incidents

Risk Factor Category	Specific Factors & Metrics	Data Source / Method
Human & Organizational	24 human factors (e.g., improper operation), 7 management factors (e.g., inadequate supervision) identified from 481 accident records [28].	Cognitive Reliability and Error Analysis Method (CREAM) [28].
Technical & Environmental	17 material/machine conditions, 20 environmental conditions identified [28]. Dynamic risk values calculated via network models [28].	Construction of a Chemical Enterprise Safety Risk Network (CESRN) [28].
Laboratory-Specific	Material Factor (MF), Process Hazard Factor, Quantity Hazard Factor derived from properties of hazardous chemicals [80].	Adaptation of the Mond Index for laboratory quantitative risk assessment [80].

Experimental Protocols for Key Supporting Methodologies

Protocol 1: Constructing a Chemical Enterprise Safety Risk Network (CESRN)

This protocol quantifies interrelationships between risk factors [28].

• Data Collection: Compile a minimum of 400-500 standardized reports of chemical safety production accidents.
• Factor Extraction: Apply the Cognitive Reliability and Error Analysis Method (CREAM) to each report to extract antecedent risk factors and their causal sequences (accident chains).
• Expert Validation: Submit a 10% sample of extracted chains for expert review. Proceed only if consistency (pass rate) exceeds 80%.
• Network Definition: Define the network G, where nodes are unique risk factors or accident outcomes. Establish an adjacency matrix M where element m_ij represents the connection strength from node i to node j.
• Calculate Edge Weights: For each potential edge, calculate the co-occurrence rate w_ij = m_ij / (m_i + m_j - m_ij), where m_i and m_j are node frequencies, and m_ij is their co-occurrence frequency. Set e_ij to 1 if a causal link exists in any accident chain, else 0. The final edge weight is m_ij = w_ij * e_ij.
• Dynamic Risk Calculation: Assign initial dynamic risk values to nodes based on factor state. Model risk propagation through the network using designed algorithms to simulate accident evolution.

Protocol 2: Dynamic Laboratory Risk Assessment Using the Mond Index

This protocol adapts an industrial index for real-time laboratory risk quantification [80].

• Parameter Identification:
  • Material Factor (MF): Determine for each hazardous chemical based on its intrinsic fire, explosion, and toxicity hazards.
  • Process Hazard Factor (F1): Assess points for operating conditions (e.g., pressure, temperature), reaction hazards, and equipment constraints.
  • Quantity Hazard Factor (F2): Calculate based on the total inventory of hazardous chemicals present.
  • Safety Compensation Factors (F3): Evaluate points for fire protection, emergency systems, personnel training, and safety procedures.
• Index Calculation:
  • Compute the DOW Fire & Explosion Index (F&EI): F&EI = MF * F1 * F2.
  • Compute the Mond Toxicity Index (TI): TI = (MF * F1 * F2 * Q) / (F3_t), where Q is a quantity factor and F3_t is toxicity compensation.
  • Compute the Overall Risk Index (RI): RI = F&EI * TI.
• Dynamic Updating: Establish a digital inventory and condition monitoring system to update parameters like chemical quantity (F2) and equipment status (part of F1) in real-time, enabling the RI to reflect current risk levels.

Protocol 3: Text Mining Chemical Accident Reports Using an LDA Topic Model

This protocol automates the extraction of latent risk themes from unstructured accident reports [81].

• Corpus Construction: Collect 500+ official chemical accident investigation reports. Extract the "Accident History" and "Cause Analysis" sections to form the core text corpus.
• Domain-Specific Preprocessing:
  • Segmentation: Use the Jieba tokenizer in Python, integrated with a custom domain dictionary of chemical engineering terms (e.g., "distillation tower").
  • Normalization: Apply a synonym dictionary to standardize varied terms (e.g., "pipe" and "pipeline").
  • Cleaning: Use an expanded deactivation dictionary to remove irrelevant stop words and symbols.
• Model Training: Apply the Latent Dirichlet Allocation (LDA) algorithm to the processed corpus. Use coherence scores to determine the optimal number of thematic topics (K).
• Factor Identification: Label each generated topic (e.g., "equipment failure during high-temperature operation") and extract its top keywords. These topics represent recurring risk factor clusters.
• Network Analysis: Feed identified factors into a Bayesian Network model to analyze probabilistic relationships and identify critical causal paths leading to accidents.

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 5: Key Reagents, Software, and Methodological Tools

Item/Tool Name	Primary Function in Chemical Risk Research	Example Use Case / Note
VOSviewer	Software for constructing and visualizing bibliometric networks based on co-citation or co-occurrence data.	Creating thematic clusters from SEM-derived literature databases [79].
CREAM (Cognitive Reliability and Error Analysis Method)	A human reliability assessment method for identifying and modeling human error in complex systems.	Extracting human and organizational risk factors from chemical accident reports [28].
Mond Index Parameters (MF, F1, F2, F3)	A set of quantitative factors for calculating fire, explosion, and toxicity indices.	Performing dynamic risk assessment in chemical laboratories [80].
Jieba Tokenizer	A Python library for Chinese text segmentation, critical for processing non-English safety reports.	Preprocessing Chinese chemical accident reports for text mining [81].
Latent Dirichlet Allocation (LDA)	A generative statistical model for discovering abstract "topics" within a collection of documents.	Identifying latent risk factor themes from a corpus of accident reports [81].
Bayesian Network (BN) Software (e.g., Netica, GeNIe)	Platforms for building and reasoning with probabilistic graphical models.	Analyzing causal relationships and sensitivity between risk factors identified via SEM or text mining [81].

Integrated Workflow Visualizations

Systematic Evidence Mapping with Stakeholder Integration

Core Gears of Effective Stakeholder Engagement [77]

Systematic Evidence Maps represent a transformative tool for navigating the expansive evidence base in chemical risk management. Their efficacy, however, is maximized only when their development is continuously informed by diverse stakeholder perspectives. This integration ensures that the mapped evidence is not only scientifically robust but also directly relevant, usable, and impactful for decision-makers. The concurrent advancement of quantitative risk modeling techniques—from complex network analysis to dynamic indexing and machine learning—provides a powerful suite of methods to translate mapped evidence into actionable risk insights. For the research community, adopting these integrated practices of engaged scholarship and systematic evidence synthesis is paramount for generating science that effectively supports the protection of human health and the environment from chemical risks.

Validating SEMs: Comparative Analysis and Regulatory Adoption

In the field of chemical risk management and drug development, the volume of toxicological and epidemiological data is vast and growing exponentially [2]. Navigating this complex evidence landscape to inform regulatory decisions and research priorities requires rigorous, transparent, and fit-for-purpose synthesis methodologies. Two cornerstone approaches are Systematic Reviews (SRs) and Systematic Evidence Maps (SEMs). While both employ systematic and reproducible methods to minimize bias, their purposes and outputs differ significantly [82]. SRs are designed to answer specific, focused research questions—such as the efficacy of a treatment or the risk posed by a specific chemical exposure—by critically appraising and synthesizing all relevant evidence, often culminating in a quantitative meta-analysis [83] [84]. In contrast, SEMs are exploratory tools that aim to map the broader research landscape. They systematically catalog and categorize available evidence on a wider topic (e.g., the health effects of a class of chemicals) to identify trends, densities of research, and critical knowledge gaps, often without performing a detailed synthesis of study findings [16] [2]. This article details the methodologies, strengths, limitations, and optimal use cases for both SRs and SEMs, providing researchers and risk assessors with clear protocols to guide their application within a strategic framework for evidence-based decision-making.

The foundational distinction between SRs and SEMs lies in their primary objective, which dictates every subsequent step in their workflow. An SR seeks to provide a definitive, synthesized answer, while an SEM seeks to create a structured, queryable database of the evidence landscape.

1.1 The Systematic Review (SR) Workflow SRs follow a linear, phase-gated protocol designed to minimize bias and produce a reliable conclusion to a precise question. The gold-standard framework is outlined by organizations like Cochrane and is typically reported following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [83] [82].

• 1. Problem Formulation & Protocol Development: The process begins with a narrowly defined research question, commonly structured using the PICO framework (Population, Intervention/Exposure, Comparator, Outcome) [83]. A detailed, publicly registered protocol is developed a priori, specifying all methodological steps.
• 2. Comprehensive Search & Study Retrieval: A systematic search is executed across multiple bibliographic databases (e.g., PubMed/MEDLINE, Embase, Cochrane Central) and gray literature sources. The search strategy is documented exhaustively for reproducibility [83].
• 3. Screening & Selection: Identified records are screened against pre-defined eligibility criteria (inclusion/exclusion) in two stages: title/abstract and full-text. This is typically performed by multiple independent reviewers [84].
• 4. Data Extraction & Critical Appraisal: Data from included studies are extracted into standardized forms. Simultaneously, the methodological quality and risk of bias of each study are rigorously assessed using tools like the Cochrane Risk of Bias tool [83].
• 5. Synthesis & Analysis: Extracted data are synthesized. For quantitative data from sufficiently homogeneous studies, a meta-analysis is conducted using statistical software (e.g., RevMan, R) to calculate a pooled effect estimate [83] [84]. If meta-analysis is not appropriate, a narrative synthesis is performed.
• 6. Reporting & Interpretation: Findings are reported with conclusions graded on the strength and quality of the synthesized evidence.

1.2 The Systematic Evidence Map (SEM) Workflow SEMs employ a similar systematic rigor in search and screening but diverge in analysis and output. Their workflow is geared toward categorization and visualization to inform future research or review priorities [16] [2].

• 1. Problem Formulation & Scoping: The research question is broader, aiming to "map" a research field. The focus is on defining key concepts and the boundaries of the map rather than a specific PICO question.
• 2. Comprehensive Search & Study Retrieval: Mirroring SRs, a systematic search is conducted across multiple sources to capture the breadth of the topic [2].
• 3. Screening & Selection: Studies are screened against broader, more inclusive criteria designed to capture the scope of the field rather than to select for high-quality, directly comparable studies.
• 4. Data Extraction & Coding (Categorization): A critical distinction from SRs: instead of detailed extraction of results for synthesis, data is extracted to describe key characteristics of the study (e.g., chemical studied, organism, endpoint, study design). This data is then "coded" using a controlled vocabulary or taxonomy to enable grouping and comparison [2].
• 5. Database Creation & Visualization: The coded data is populated into a structured database or, optimally, a knowledge graph that allows for complex querying [2]. The output is not a statistical synthesis but a set of visualizations—such as heatmaps, bubble plots, or interactive network diagrams—that illustrate the volume and distribution of research across the mapped categories [16].
• 6. Gap Analysis & Reporting: The final report interprets the visualizations to identify well-studied areas and, more importantly, evidence clusters, white spaces, and knowledge gaps that can guide targeted primary research or the commissioning of future SRs.

Table 1: Core Methodological Differences Between Systematic Reviews and Systematic Evidence Maps

Methodological Aspect	Systematic Review (with Meta-Analysis)	Systematic Evidence Map
Primary Objective	Answer a specific question with a synthesized conclusion.	Catalog and characterize the evidence landscape to identify trends and gaps.
Research Question	Narrow, focused (e.g., PICO format).	Broad, scoping.
Inclusion Criteria	Strict, based on PICO and study design to ensure comparability.	Broad, to capture the full scope of literature on a topic.
Critical Appraisal	Mandatory; risk of bias assessment is central to interpreting findings.	Optional; may be done to categorize study reliability but not to exclude studies.
Data Extraction Focus	Detailed extraction of results (means, effects, outcomes) for synthesis.	Extraction of descriptive metadata (study characteristics) for categorization.
Core Analytical Method	Quantitative synthesis (meta-analysis) or detailed narrative synthesis.	Descriptive synthesis, categorization, coding, and visualization.
Key Output	Pooled effect estimate (e.g., risk ratio), narrative conclusion, evidence grade.	Searchable database, visual maps (heatmaps, networks), gap analysis report.

Experimental Protocols for Core Methodologies

2.1 Protocol for Conducting a Systematic Review for Chemical Risk Assessment

• Step 1 – Protocol Registration: Register the review protocol on a platform like PROSPERO or the Open Science Framework prior to commencing the search. The protocol must detail the PICO question, search strategy, eligibility criteria, and planned analysis methods [83].
• Step 2 – Development of the Search Strategy: Collaborate with a research librarian. Translate the PICO elements into search terms using controlled vocabularies (e.g., MeSH for PubMed) and free-text keywords. Combine terms with Boolean operators (AND, OR). Test and validate the search sensitivity and precision. Document the final strategy for each database exhaustively [83].
• Step 3 – Study Screening with Dedicated Software: Import all retrieved citations into reference management software (EndNote, Zotero) for deduplication. Use systematic review software (Rayyan, Covidence) for the blinded screening process. Two independent reviewers screen titles/abstracts, then full texts, resolving conflicts by consensus or a third reviewer [84].
• Step 4 – Data Extraction & Risk of Bias Assessment: Develop and pilot a standardized data extraction form. Extract data on study design, population/exposure details, outcomes, and results. In parallel, two reviewers independently assess the risk of bias for each study using a domain-based tool appropriate to the study design (e.g., ROBINS-I for non-randomized studies) [83].
• Step 5 – Statistical Synthesis (Meta-Analysis): For quantitative synthesis, use software such as R (with metafor package) or RevMan. Calculate effect sizes (e.g., risk ratios, mean differences) for each study. Choose a statistical model (fixed- or random-effects) based on the assessment of heterogeneity (I² statistic). Generate forest plots to visualize pooled effect estimates and confidence intervals. Conduct sensitivity and subgroup analyses to explore heterogeneity [83] [84].

2.2 Protocol for Developing a Systematic Evidence Map

• Step 1 – Scoping & Framework Development: Conduct preliminary scoping searches to define the map's boundaries. Develop a coding framework (taxonomy) for key dimensions (e.g., chemical classes, health outcomes, exposure routes, mechanistic endpoints). This framework is the backbone of the SEM and should be informed by the research and policy context [16] [2].
• Step 2 – Systematic Search & Screening: Follow steps analogous to an SR (Protocol 2.1, Steps 2-3) but with broader eligibility criteria. The goal is to be inclusive rather than exclusive, capturing all relevant research within the defined scope.
• Step 3 – Data Extraction & Coding: Extract descriptive data aligned with the coding framework. This is a categorization exercise. For example, a study on "Bisphenol A and prenatal developmental toxicity in rat models" would be coded for: Chemical=Bisphenol A (subcategory: endocrine disruptor); Organism=Mammalian (Rat); Endpoint=Developmental Toxicity; Study Type=In vivo animal study. Coding allows disparate studies to be grouped and compared [2].
• Step 4 – Database/KNOWLEDGE Graph Construction: Move beyond simple spreadsheets. Structure the coded data in a relational database or, more powerfully, as a knowledge graph. Graph databases can intuitively represent the complex relationships between chemicals, molecular targets, adverse outcomes, and study types, facilitating advanced querying and pattern recognition [2].
• Step 5 – Visualization & Gap Analysis: Use visualization tools (e.g., Tableau, R ggplot2, Gephi for networks) to create maps. A heatmap can show research volume (number of studies) across a matrix of chemical classes vs. health outcomes, instantly revealing densely populated and empty cells. Interpret these visuals to articulate specific, actionable research needs [16].

Graphviz diagram 1: Systematic Review Linear Workflow (78 characters)

Graphviz diagram 2: Systematic Evidence Map Iterative Workflow (80 characters)

Strengths and Limitations: A Comparative Analysis

Table 2: Comparative Strengths and Limitations of SEMs and SRs

Aspect	Systematic Evidence Maps (SEMs)	Systematic Reviews (SRs)
Core Strengths	• Identifies Knowledge Gaps: Excel at revealing under-researched areas to guide future work [16].• Handles Broad/Complex Topics: Manages large, heterogeneous evidence bases not suitable for direct synthesis [2].• Informs Strategic Planning: Outputs directly useful for funders and agencies to prioritize research and review investments [2].• Foundation for SRs: Provides an objective basis for selecting focused questions worthy of a full SR [16].	• Provides Definitive Answers: Offers the highest level of evidence for specific, answerable questions [83] [84].• Quantifies Effects: Meta-analysis increases statistical power and precision of effect estimates [83].•Reduces Bias: Rigorous methodology minimizes selection and interpretation bias.• Directly Informs Practice/Policy: Conclusions can be integrated into clinical guidelines and risk assessments [82].
Key Limitations	• No Synthesized Conclusion: Does not provide an answer on the magnitude or direction of effects [16].• Resource Intensive: Broad searches and coding are still time-consuming [2].• Challenges in Visualization: Effectively communicating complex mapped data can be difficult.• Less Familiar to Decision-Makers: May be misunderstood as an incomplete review.	• Narrow Scope: A single SR addresses only one focused question, providing a limited view of a broader issue.• Rapid Obsolescence: Can become outdated quickly with new evidence [84].• Resource Intensive: Requires significant time and expertise, especially for meta-analysis [85].• May Be Impossible: If studies are too heterogeneous, a meaningful quantitative synthesis cannot be performed.

Appropriate Use Cases in Chemical Risk Management and Drug Development

The choice between an SEM and an SR is not a matter of hierarchy but of strategic alignment with the research or decision-making phase.

Use a Systematic Evidence Map When:

• Scoping a New Field: For a new chemical class (e.g., "What are the known and potential health effects of per- and polyfluoroalkyl substances (PFAS)?") where the evidence is vast and fragmented, an SEM can chart the terrain [2].
• Informing a Research Agenda: A funding agency or research institute can use an SEM to identify which specific chemical-outcome pairs lack sufficient data, ensuring resources target the most critical gaps [16].
• Prioritizing Chemicals for Risk Assessment: Regulatory bodies can use SEMs to triage thousands of chemicals by mapping available toxicity data, focusing detailed assessment resources on those with suggestive but incomplete evidence profiles [86].
• Supporting Mechanistic Hypothesis Generation: By mapping studies across biological levels (e.g., molecular initiation events, cellular key events, organ outcomes), SEMs can inform the development of Adverse Outcome Pathways (AOPs) [86].

Use a Systematic Review (with Meta-Analysis) When:

• Answering a Specific Risk Question: For a well-defined, high-priority question (e.g., "Does occupational exposure to Chemical X increase the risk of liver cancer in humans?"), an SR provides the definitive evidence synthesis for a risk assessment [83].
• Resolving Scientific Controversy: When primary studies on a chemical's toxicity show conflicting results, an SR and meta-analysis can quantify the overall effect and explore sources of heterogeneity [84].
• Supporting Regulatory Decision-Making: To set an official reference dose (RfD) or derive a health-based guidance value, regulators require a synthesized, bias-adjusted estimate of the critical effect, which an SR provides [83].
• Evaluating Drug Safety: In drug development, an SR can comprehensively synthesize evidence on a known adverse event across preclinical and clinical studies.

Table 3: Decision Guide for Selecting an Evidence Synthesis Method

Decision Factor	Lean Toward a Systematic Evidence Map (SEM)	Lean Toward a Systematic Review (SR)
Primary Goal	Exploration, landscape mapping, gap identification.	Answering a specific question, providing a definitive conclusion.
Research Question	Broad: "What is known about...?"	Narrow: "What is the effect of X on Y?"
Evidence Base	Large, heterogeneous, unclear.	Sufficiently homogeneous for synthesis.
Time/Resources	Resources for mapping and visualization are available.	Resources for deep critical appraisal and statistical analysis are available.
Stage of Research/Policy Cycle	Early stage: agenda-setting, problem formulation.	Later stage: decision-making, guideline development.

Table 4: Key Research Reagent Solutions for Evidence Synthesis

Tool/Resource	Primary Function	Relevance to SEM/SR
Covidence / Rayyan	Web-based platforms for managing the screening and selection phase of reviews. Supports deduplication, blinded screening, and conflict resolution.	Core for both. Streamlines the most labor-intensive shared step between SEMs and SRs [83].
EndNote / Zotero / Mendeley	Reference management software. Crucial for storing retrieved citations, removing duplicates, and organizing PDFs.	Core for both. Foundational for managing the literature corpus [83].
ROSES (RepOrting standards for Systematic Evidence Syntheses)	Reporting standard specifically created for systematic maps and systematic reviews in environmental science.	Guideline for both. Ensures methodological transparency in reporting, especially for SEMs [16].
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)	The dominant reporting guideline for systematic reviews and meta-analyses.	Guideline for SR. Essential for ensuring completeness and transparency of an SR report [83] [82].
R Statistical Software (with metafor, ggplot2 packages)	Open-source software for statistical computing and graphics. metafor is a premier package for meta-analysis; ggplot2 is essential for creating publication-quality visualizations.	Core for SR analysis, Useful for SEM viz. The primary tool for statistical synthesis in SRs and for creating custom charts and heatmaps for SEMs [83].
Graph Database Platforms (e.g., Neo4j)	Databases that use graph structures (nodes, edges, properties) to represent and store data. Ideal for representing interconnected data.	Advanced for SEM. The recommended structure for creating queryable, interactive knowledge graphs from mapped evidence, capturing complex relationships [2].
PICO Portal / HAWC (Health Assessment Workspace Collaborative)	Specialized software tools designed for managing and conducting health assessments and systematic reviews, particularly for environmental chemicals.	Domain-Specific for both. Provides structured workflows and templates tailored to the needs of chemical risk assessors [86].

Integrated Application within a Chemical Risk Management Thesis

Within a thesis on systematic evidence mapping for chemical risk management, SEMs are not a competitor to SRs but a complementary, upstream strategic tool. A coherent thesis might propose and demonstrate a framework where:

• An SEM is first deployed on a major class of concern (e.g., phthalates), mapping hundreds of studies across epidemiological, in vivo, and in vitro evidence.
• The resulting visualizations and gap analysis identify a critical cluster of suggestive but inconclusive human evidence on a specific outcome (e.g., childhood asthma) paired with strong mechanistic data.
• This finding directly justifies and shapes the protocol for a subsequent, highly focused SR and meta-analysis on that specific chemical-outcome pair.
• The conclusions of the SR then feed directly into a weight-of-evidence assessment to inform regulatory risk management decisions.

This sequential, iterative approach maximizes the efficiency of limited research resources. The SEM ensures that the substantial effort required for an SR is invested in the most pressing and feasible questions, while the SR provides the definitive answers needed for action. Together, they form a powerful, evidence-driven cycle for navigating complex scientific landscapes and advancing public health protection.

Case Studies in Chemical Risk Assessment (e.g., Acrolein, Medical Cannabis)

Systematic evidence mapping (SEM) has emerged as a foundational methodology for navigating the complex data landscapes of modern chemical risk assessment. It provides a structured, transparent process to identify, categorize, and evaluate available scientific evidence, enabling the prioritization of research needs and the identification of critical data gaps for risk management decisions. This article demonstrates the application of SEM within the context of a broader thesis on systematic approaches to chemical risk management. Through two contemporary case studies—the industrial chemical acrolein and the complex mixture of medical cannabis—we illustrate how SEM frameworks guide hazard identification, dose-response analysis, and exposure assessment. These cases highlight the transition from traditional deterministic methods to probabilistic and tiered approaches that quantitatively characterize uncertainty and variability, essential for protecting public health in occupational, environmental, and consumer product contexts [87] [88].

Case Study 1: Acrolein – Probabilistic Dose-Response Assessment

Acrolein (C₃H₄O) is a highly reactive, colorless to yellow liquid with a piercing odor, widely used as an industrial biocide and chemical intermediate and formed as a byproduct of combustion [89] [90]. Its high toxicity, primarily causing severe respiratory tract irritation, necessitates precise risk assessment to establish safe exposure levels [90].

Systematic Evidence Mapping and Critical Effect Identification

An updated systematic review of the literature identified nasal lesions in rats as the most appropriate critical endpoint for inhalation risk assessment. The subchronic inhalation study by Dorman et al. (2008) was selected as the pivotal study, providing dose-response data for minimal lesions in the nasal epithelium [87] [91]. This step exemplifies the SEM process of screening and selecting high-quality, relevant studies for quantitative analysis.

Key Quantitative Data and Occupational Exposure Limits

The table below summarizes key physico-chemical properties, occupational exposure limits, and risk assessment outcomes for acrolein.

Table: Acrolein Key Properties, Exposure Limits, and Risk Assessment Values

Parameter	Value	Source/Notes
CAS Number	107-02-8	[89]
Molecular Weight	56.1 g/mol	[89]
OSHA PEL (8-hr TWA)	0.1 ppm (0.25 mg/m³)	Permissible Exposure Limit [89] [92]
NIOSH REL (10-hr TWA)	0.1 ppm (0.25 mg/m³)	Recommended Exposure Limit [89] [90]
NIOSH STEL	0.3 ppm (0.8 mg/m³)	Short-Term Exposure Limit [90]
ACGIH TLV (8-hr TWA)	0.1 ppm	Threshold Limit Value [92]
IDLH	2 ppm	Immediately Dangerous to Life or Health [90]
Probabilistic Reference Value	6 × 10⁻⁴ mg/m³	5th percentile of risk-specific dose for 1% incidence of minimal nasal lesions [87] [91]
Deterministic Reference Value	8 × 10⁻⁴ mg/m³	Derived using traditional point estimate methods [87]
Uncertainty Span (95th/5th percentile)	Factor of 137	Quantifies variability and uncertainty in the risk-specific dose distribution [87]

Detailed Experimental Protocol: Probabilistic Reference Value Derivation Using APROBA

This protocol details the application of the Approximate Probabilistic Analysis (APROBA) tool within a unified probabilistic framework [87] [91].

Objective: To derive a probabilistic reference value (pRV) for acrolein based on nasal lesion data, quantifying uncertainty and population variability.

Materials & Data Input:

• Dose-Response Data: Incidence data for minimal nasal lesions from the critical study (e.g., Dorman et al., 2008) [87].
• APROBA Software: A spreadsheet tool implementing the unified probabilistic framework.
• Input Distributions: Defined probability distributions for all uncertain and variable parameters:
  • Animal Dose-Response: Modeled using a lognormal distribution for the benchmark dose (BMD) or no-observed-adverse-effect level (NOAEL).
  • Interspecies & Intraspecies Kinetics/Dynamics: Default or compound-specific distributions for extrapolating from animal to human and accounting for human variability.
  • Exposure Duration Adjustment: Distribution to adjust from subchronic to chronic exposure.

Procedure:

• Define Risk-Specific Dose (RSD): Set the RSD as the human dose (mg/m³) at which 1% of the population is estimated to experience minimal lesions.
• Propagate Uncertainty: Use Monte Carlo simulation in APROBA to combine all input parameter distributions. This generates a probability distribution for the RSD.
• Calculate pRV: Extract the 5th percentile of the computed RSD distribution as the pRV.
• Perform Sensitivity Analysis: Recalculate the pRV under alternative assumptions (e.g., using a NOAEL instead of a BMD as the point of departure, or varying the exposure duration factor) to assess the robustness of the result [87].
• Compare with Deterministic RV: Derive a traditional deterministic reference value using single point estimates for each adjustment factor and compare with the pRV.

Workflow Diagram: Probabilistic Risk Assessment for Acrolein

The Scientist's Toolkit: Acrolein Sampling & Analysis

Table: Essential Materials for Acrolein Exposure Monitoring [89]

Item	Function
SKC 226-117 Sampler	XAD-2 tube coated with 10% 2-(hydroxymethyl)piperidine. Efficiently collects acrolein vapor from air over an 8-hour sampling period.
Personal Sampling Pump	Calibrated to a flow rate of 0.1 L/min for time-weighted average (TWA) sampling, drawing a standard volume of 48L.
Gas Chromatograph with Nitrogen-Phosphorus Detector (GC-NPD)	Analytical instrument for separating and quantifying acrolein extracted from the sampling tube. OSHA Method 52.
Matheson-Kitagawa 8014-136 Detector Tube	Direct-reading colorimetric tube for rapid, on-site screening of acrolein concentrations (approx. 0.005-1.8% range).

Case Study 2: Medical Cannabis – A Tiered Framework for Additive Risk

The legalization of medical and adult-use cannabis has created an urgent need for risk assessment frameworks tailored to inhaled cannabis concentrates (oils). This case focuses on a first-tier framework for evaluating intentionally added ingredients (e.g., terpenes, flavors), excluding cannabinoids and contaminants which require more complex assessment [88].

Evidence Mapping for Exposure and Hazard Data

A significant challenge in cannabis risk assessment is the scarcity of robust consumption data. Systematic evidence gathering for this framework incorporated previously unpublished telemetry data from over 54,000 smart vaporization devices (PAX Era) [88]. This analysis established critical exposure parameters:

• 50th percentile user: 5 mg concentrate per day.
• 95th percentile user: 57 mg concentrate per day.
• Proposed exposure for first-tier assessment: 100 mg concentrate per day, a health-protective value covering high-end use [88].

Hazard identification relies on gathering toxicological data from all available sources, including databases for occupational limits, toxicity values, and published literature, applying a tiered approach where simple thresholds (e.g., Threshold of Toxicological Concern) can screen out low-risk additives [88].

Key Quantitative Data for Cannabis Concentrate Risk Assessment

Table: Key Exposure and Risk Metrics for Cannabis Concentrate Additives

Parameter	Value	Notes / Context
Proposed Daily Exposure (First-Tier)	100 mg concentrate/day	Health-protective assumption for risk assessment of additives [88].
Typical Additive Concentration	5–15% by weight	Common range for terpenes/flavors in cannabis concentrates [88].
Reported Heart Attack Risk (Users <50)	6-fold increase	Retrospective study finding vs. non-users [93].
LOAEL for Δ9-THC (Acute)	2.5 mg/day	Lowest Observed Adverse Effect Level established by EFSA [94].
Proposed Serious Risk Threshold in CBD Oil	500 mg Δ9-THC/kg	Level below which LOAEL is not exceeded in typical consumption scenarios [94].

Detailed Protocol: First-Tier Risk Assessment for Cannabis Concentrate Additives

Objective: To provide a pragmatic, semi-quantitative method for regulators and manufacturers to prioritize cannabis concentrate additives for acceptance, elimination, or advanced evaluation [88].

Materials & Data Input:

• Chemical Identity: Precise name and CAS number of the additive.
• Concentration in Formulation: Maximum intended percentage by weight in the cannabis concentrate.
• Toxicological Data: Compiled from authoritative sources (e.g., EPA IRIS, IARC, EFSA, COSMOS). Priority data points include: Occupational Exposure Limits (OELs), Oral Reference Doses (RfD), Carcinogenicity classifications, and genotoxicity data.
• Consumption Assumption: Use the default daily exposure of 100 mg of cannabis concentrate [88].

Procedure:

• Calculate Daily Intake of Additive:
  • Intake (mg/day) = Daily Concentrate Exposure (100 mg) × (Additive Concentration % / 100).
• Apply Threshold of Toxicological Concern (TTC):
  • If the calculated daily intake is below the relevant inhalation TTC (e.g., Cramer Class-specific values), the additive may be considered low priority for further assessment.
• Compare to Available Toxicity Values:
  • If an OEL or derived no-effect level is available, calculate a margin of exposure (MoE).
  • MoE = (Toxicity Value, e.g., OEL-derived dose) / (Calculated Daily Intake).
  • Apply a predefined, health-protective target MoE (e.g., 100-1000). An MoE greater than the target suggests acceptable risk at the first tier.
• Evaluate Hazard Flags:
  • Screen for high-potency hazards: Is the additive a known allergen, respiratory sensitizer, genotoxin, or carcinogen? Presence of such flags may trigger elimination or immediate higher-tier assessment.
• Decision & Prioritization:
  • Accept: Additive passes TTC and MoE criteria, with no hazard flags.
  • Eliminate: Additive shows high-potency hazard flags (e.g., known carcinogen).
  • Further Evaluation: Additive falls into a data gap, has an insufficient MoE, or requires more refined exposure or toxicity data.

Workflow Diagram: First-Tier Risk Assessment for Cannabis Additives

The Scientist's Toolkit: Cannabis Product Risk Assessment

Table: Key Tools for Cannabis Product Risk & Exposure Analysis

Item / Concept	Function in Risk Assessment
Telemetry-Enabled Vaporizer Data	Provides real-world, anonymized consumption data (puff duration, frequency, estimated mass) to characterize user exposure patterns, moving beyond theoretical estimates [88].
In Vitro New Approach Methodologies (NAMs)	Cell-based assays (e.g., for genotoxicity, cytotoxicity) used to generate hazard data for additives lacking traditional toxicology studies, crucial given data gaps for many cannabis-related chemicals [88].
Threshold of Toxicological Concern (TTC)	A screening tool that establishes a human exposure threshold below which there is a low probability of risk, even in the absence of chemical-specific data. Used to prioritize resources [88].
Margin of Exposure (MoE) Analysis	A core risk characterization metric comparing a point of departure from toxicological data (e.g., BMD) to the estimated human exposure. A larger MoE indicates lower risk [94].

Integrated Discussion: Systematic Mapping as the Bridge

These case studies demonstrate that systematic evidence mapping is not a peripheral activity but the central scaffold supporting robust chemical risk assessment.

• For Acrolein, SEM facilitated the selection of the most relevant toxicological endpoint and study from the existing literature, which then fed directly into a state-of-the-art probabilistic framework. This approach quantitatively expressed uncertainty (spanning a factor of 137), providing risk managers with a more complete picture than a single deterministic value [87] [91].
• For Cannabis Additives, the "first-tier" framework is itself an application of SEM principles: it begins with systematically gathering available hazard and exposure data before applying sequential filters (TTC, MoE, hazard flags) to triage substances. It explicitly addresses data scarcity by defining clear decision points for when existing evidence is sufficient or when generation of new evidence is required [88].

The contrasting regulatory contexts—a well-established industrial chemical versus an emerging consumer product—highlight SEM's versatility. In both cases, a systematic approach transforms fragmented data into actionable knowledge, enabling transparent, science-based decisions that are critical for protecting public health in the face of uncertainty.

Assessing the Impact of SEMs on Regulatory Decisions and Priority Setting

Within the domain of chemical risk management, regulators and researchers are confronted with vast, fragmented, and often contradictory bodies of scientific literature. Traditional systematic reviews (SRs), while robust, are resource-intensive and designed to answer narrowly focused questions [1]. This creates an evidence-to-decision gap, particularly for agencies conducting priority setting, horizon scanning, or evaluating broad regulatory frameworks like the Toxic Substances Control Act (TSCA) in the U.S. or the EU's REACH regulation [1].

Systematic Evidence Maps (SEMs) have emerged as a critical tool to bridge this gap. An SEM is defined as a queryable database of systematically gathered research that characterizes the broad landscape of available evidence on a given topic [1]. Unlike an SR, an SEM does not synthesize findings to estimate an effect size; instead, it catalogs what evidence exists, where it exists, and identifies key trends, clusters, and, crucially, evidence gaps [95]. This structured, evidence-based overview enables a more efficient and transparent allocation of resources—guiding whether to commission a full SR, initiate new primary research, or proceed directly to risk management decisions [16]. The ongoing evolution of regulatory procedures, such as the 2024-2025 amendments and proposed revisions to the TSCA risk evaluation framework, underscores the need for tools like SEMs to provide a clear, auditable basis for defining the scope and focus of such assessments [96] [97].

Applications in Regulatory Decision-Making and Priority Setting

SEMs directly support several core functions in chemical risk governance by transforming unstructured literature into a structured, actionable evidence asset. Their primary applications are detailed below.

Table 1: Key Regulatory Applications of Systematic Evidence Maps (SEMs)

Application Area	Specific Regulatory Use Case	Impact on Decision-Making
Research Prioritization & Agenda Setting	Identifying clusters of evidence for high-volume chemicals (e.g., phthalates, PFAS) and flagging understudied substances or health endpoints.	Prevents redundant research and directs funding to critical evidence gaps, ensuring efficient use of scientific resources [1] [16].
Scoping for Systematic Reviews & Risk Evaluations	Defining the boundaries and populations, exposures, comparators, and outcomes (e.g., for a TSCA risk evaluation).	Provides a defensible rationale for the scope of a subsequent deep-dive assessment, improving transparency and stakeholder acceptance [96] [97].
Informing Regulatory Framework Updates	Mapping evidence on emerging exposure pathways (e.g., nano-plastics) or novel toxicity mechanisms to assess the adequacy of existing testing guidelines.	Supports forward-looking "trendspotting" to ensure regulatory frameworks keep pace with advancing science [1].
Stakeholder Engagement & Transparency	Serving as a publicly accessible, interactive evidence platform that catalogs all considered studies, including those excluded from further review.	Builds trust in the regulatory process by making the evidence base visible and accessible, allowing for independent scrutiny [98] [16].

Protocol for Conducting a Systematic Evidence Map

The methodological rigor of an SEM is what distinguishes it from a traditional literature review. The following protocol, synthesized from current guidance, provides a stepwise framework applicable to chemical risk management questions [16].

Stage 1: Definition of Scope and Key Elements

• Objective: Formulate a clear, broad question relevant to policy (e.g., "What is the available evidence on the neurodevelopmental effects of organophosphate flame retardants?").
• Key Actions: Establish an advisory panel including subject matter experts and end-users (e.g., risk managers). Define the key elements (population, exposure, comparator, outcome) but with broader inclusivity than an SR. Pre-register the protocol on platforms like PROSPERO or the Open Science Framework.

Stage 2: Systematic Search Strategy

• Objective: To capture a comprehensive, unbiased sample of the relevant literature.
• Key Actions:
  • Develop search strings with a librarian/information specialist, using controlled vocabularies (e.g., MeSH) and free-text terms.
  • Search multiple electronic databases (e.g., PubMed, Web of Science, Embase, TOXLINE).
  • Supplement with grey literature searches from regulatory agency websites (e.g., EPA, ECHA) and relevant conference proceedings.
  • Document the full search strategy for every database.

Stage 3: Screening & Study Selection

• Objective: To filter search results against pre-defined eligibility criteria in a consistent, reproducible manner.
• Key Actions: Use dual-independent screening for a subset of records at both title/abstract and full-text levels to ensure reliability. Employ systematic review software (e.g., Rayyan, Covidence, DistillerSR) to manage the process and record exclusion reasons.

Stage 4: Data Extraction & Coding

• Objective: To characterize each included study using a standardized, pilot-tested coding tool.
• Key Actions: Extract descriptive metadata (author, year), study design (in vivo, in vitro, epidemiological), population/exposure details, and measured outcomes. Code for evidence map-specific features, such as the direction of effect (positive, negative, null) or risk of bias indicator, if required for prioritization purposes [16].

Stage 5: Data Visualization & Narrative Synthesis

• Objective: To translate the coded database into an accessible format that highlights evidence patterns and gaps.
• Key Actions:
  • Generate visualizations such as interactive evidence atlases, heatmaps (showing volume of evidence by chemical and outcome), or flow diagrams.
  • Produce a narrative report that summarizes the distribution and characteristics of the evidence base, explicitly stating where evidence is sufficient or lacking.

Stage 6: Reporting & Archiving

• Objective: To ensure transparency, reproducibility, and long-term utility.
• Key Actions: Publish the final SEM report and make the underlying coded database publicly available in a repository. The report should explicitly state how the findings can be used to inform subsequent research or decision-making steps [16].

Visualizing the SEM Workflow and Decision Pathways

Diagram 1: SEM Development and Regulatory Integration Workflow [1] [16] This diagram outlines the iterative process of creating an SEM and its direct inputs into regulatory activities.

Diagram 2: SEM-Informed Decision Pathway for Chemical Assessment [96] [97] This diagram illustrates how an SEM directly informs pivotal choices in a chemical risk evaluation process, such as those under TSCA.

Table 2: Essential Toolkit for Conducting Systematic Evidence Maps

Tool Category	Specific Item / Solution	Function & Rationale
Protocol Development	PICO/PECO Framework; PRISMA-ScR/PRISMA-SEM Checklist	Structures the research question and ensures comprehensive reporting of methods [16].
Search & Retrieval	Boolean Operators; Database APIs (e.g., PubMed E-utilities); Reference Management Software (EndNote, Zotero)	Enables precise, replicable searches and efficient management of retrieved citations.
Screening & Deduplication	Rayyan; Covidence; DistillerSR; ASReview (AI-powered)	Facilitates blind dual screening, conflict resolution, and deduplication, critical for reducing bias [16].
Data Extraction & Coding	Custom-built Google Sheets or Excel forms; Systematic Review software modules; REDCap	Provides structured, pilot-tested interfaces for consistent data capture from primary studies.
Visualization & Analysis	R (ggplot2, plotly); Python (matplotlib, seaborn); Tableau; EviAtlas	Generates interactive heatmaps, bubble plots, and evidence atlases to communicate patterns and gaps [99] [100].
Reporting & Archiving	Institutional Repositories (e.g., Zenodo); Interactive Web Platforms (e.g., ESRI StoryMaps)	Ensures long-term access to the SEM database and findings, fulfilling transparency requirements [16].

Emerging Standards and Reporting Guidelines for Evidence Maps

Systematic evidence mapping (SEM) has emerged as a foundational methodology for navigating the expansive and heterogeneous data landscape of environmental health and chemical risk assessment [2]. Within the context of chemical risk management research, evidence maps function as queryable databases that systematically gather, structure, and characterize the available scientific literature on given chemical substances or classes [2]. Their primary value lies in providing a comprehensive overview of an evidence base, enabling the identification of knowledge clusters suitable for full systematic review and critical gaps warranting further primary research [101].

This application is increasingly critical as regulatory agencies like the U.S. Environmental Protection Agency (EPA) incorporate more evidence-based approaches into their frameworks. For instance, the EPA's risk evaluation process under the Toxic Substances Control Act (TSCA) requires determinations based on the "weight of scientific evidence," a standard that demands transparent and systematic handling of all relevant data [69] [102]. Recent announcements of risk evaluations for known or probable carcinogens, such as vinyl chloride and benzene, underscore the practical demand for robust methods to organize and assess large volumes of toxicological and exposure science [49]. Emerging standards and reporting guidelines for creating these maps are therefore essential to ensure they are methodologically sound, reproducible, and effectively support regulatory and research decision-making.

Emerging Standards for Evidence Map Development and Reporting

The development of a systematic evidence map is governed by protocols designed to maximize transparency, minimize bias, and ensure utility for end-users. Drawing from established practices in environmental evidence and adapting to the specific needs of chemical risk assessment, several key standards have crystallized.

Table 1: Emerging Standards for Systematic Evidence Mapping in Chemical Risk Research

Standard Category	Core Principle	Application in Chemical Risk Management	Reporting Guideline
Protocol Pre-registration	A detailed, publicly available plan defining the map's scope, questions, and methods before work begins.	Justifies the focus on specific chemicals, health endpoints (e.g., carcinogenicity, developmental toxicity), or exposure pathways relevant to TSCA evaluations [49].	Document the PECO/PECO (Population, Exposure, Comparator, Outcome) elements, search strategy, and inclusion/exclusion criteria.
Systematic Search & Screening	Reproducible, comprehensive searches across multiple bibliographic databases and grey literature sources.	Ensures capture of all studies on high-priority substances (e.g., acetaldehyde, acrylonitrile) for hazard and exposure assessment [69] [49].	Report databases searched, search strings, date of search, and a flow diagram of study screening and selection.
Data Extraction & Coding	Use of controlled vocabularies and ontologies to categorize study design, population, exposure, outcome, and other metadata.	Enables comparison of heterogeneous studies (e.g., in vivo, in vitro, epidemiological) for a single chemical across its conditions of use [69] [2].	Publish the coding framework (codebook) and make the extracted database publicly accessible.
Critical Appraisal	Assessment of individual study reliability or risk of bias within the map's context.	Informs the "weight of evidence" approach by tagging studies with quality indicators, as required in TSCA science standards [69] [102].	Report the appraisal tool used (e.g., OHAT, Klimisch) and summarize the distribution of study reliability.
Visual Reporting & Accessibility	Interactive visualizations and databases that allow users to explore the evidence base.	Facilitates rapid identification of data-rich areas for risk characterization and gaps for future research, aligning with EPA's use of evidence maps [49] [101].	Provide interactive heatmaps, evidence atlases, and structured databases via tools like EviAtlas [101].

A significant methodological advancement is the shift from rigid, schema-first databases to flexible, graph-based data models. Traditional flat tables struggle to represent the complex, interconnected relationships inherent in toxicological data (e.g., linking a chemical to multiple metabolites, molecular targets, and adverse outcomes). Knowledge graphs, which store data as networks of nodes and relationships, are better suited for this task, promoting interoperability and scalable exploration of the evidence base [2].

Experimental Protocols for Evidence Synthesis

Protocol 3.1: Conducting a Traditional Systematic Evidence Map This protocol outlines the steps for creating a systematic evidence map using a standardized, linear workflow.

Workflow for a Traditional Systematic Evidence Map

• Define Scope and Protocol: Formulate the primary map question using PECO elements. Document the search strategy, inclusion/exclusion criteria, and coding framework. Register the protocol on a platform like PROSPERO or the Open Science Framework [2].
• Systematic Search: Execute the search across relevant databases (e.g., PubMed, Scopus, Embase, TOXLINE) using tailored strings. Supplement with grey literature searches from regulatory agency websites (e.g., EPA, ECHA) and industry reports. Record the search date and results [49].
• Screening: Use dedicated software (e.g., Rayyan, Covidence) to deduplicate records. Conduct blind screening at the title/abstract and full-text levels against predefined criteria. Disagreements are resolved by a third reviewer. A flow diagram is maintained.
• Data Extraction & Coding: Using a pre-piloted form, extract metadata from included studies. Code data into structured fields (e.g., chemical, study type, species, endpoint, exposure route) adhering to a controlled vocabulary or ontology where possible [2].
• Critical Appraisal: Assess the reliability or risk of bias for each study using a domain-appropriate tool (e.g., NTP/OHAT for toxicology studies). This assessment is used for descriptive characterization, not as a filter for inclusion.
• Database Assembly & Visualization: Compile extracted and coded data into a master database. Generate visual summaries using tools like EviAtlas to produce heatmaps (cross-tabulating variables), bar charts (showing evidence volume over time), and interactive evidence atlases for geospatial data [101].
• Reporting: Publish a final report describing the process, presenting the visualizations, and discussing the evidence clusters and gaps. The underlying interactive database is made publicly accessible for user querying [101].

Protocol 3.2: Implementing a Knowledge Graph-Based Evidence Map This protocol describes an advanced method for building a semantically structured evidence map that captures complex relationships.

Workflow for a Knowledge Graph-Based Evidence Map

• Ontology Selection & Development: Select and extend existing ontologies (e.g., Chemical Entities of Biological Interest (ChEBI) for chemicals, Adverse Outcome Pathway (AOP) ontology for toxicological mechanisms) to create a unified semantic framework for the evidence domain [2].
• Data Ingestion & Node/Relationship Extraction: Ingest studies and data from Protocol 3.1, Step 4. Use natural language processing (NLP) tools, aided by the ontology, to automatically identify and extract entities (nodes: Chemical, Study, Endpoint, Gene) and their predefined relationships (edges: causes, associates_with, measures).
• Graph Database Population: Populate a graph database (e.g., Neo4j, Amazon Neptune) with the extracted nodes and relationships. This schemaless approach allows for the flexible addition of new entity and relationship types without restructuring the entire database [2].
• Querying & Exploration: End-users, such as risk assessors, can query the graph using languages like Cypher or SPARQL to ask complex questions (e.g., "Find all studies linking Chemical X to oxidative stress outcomes in mammalian in vivo models, and show connected molecular initiating events").
• Visualization & Interface Development: Implement a front-end application that allows for interactive exploration of the knowledge graph, enabling dynamic filtering, pathfinding, and visualization of evidence networks.

The Scientist's Toolkit: Essential Materials for Evidence Mapping

Table 2: Research Reagent Solutions for Systematic Evidence Mapping

Tool Category	Item Name	Function in Evidence Mapping	Example/Reference
Protocol & Reporting	PRISMA-ScR & ROSES	Reporting checklists to ensure transparency and completeness in the published map report.	Equator Network [103]
Search Management	Bibliographic Databases	Sources for primary research literature (toxicology, environmental science, medicine).	PubMed, Scopus, TOXLINE, Web of Science
	Grey Literature Repositories	Sources for regulatory studies, dissertations, and unpublished data.	EPA ChemView, ECHA database, ProQuest Dissertations
Screening & Extraction	Dedicated Systematic Review Software	Platforms for collaborative screening, data extraction, and workflow management.	Rayyan, Covidence, CADIMA
Data Structure & Coding	Toxicological Ontologies	Controlled vocabularies to semantically code and link evidence concepts.	AOP Wiki, ChEBI, OBO Foundry ontologies [2]
Data Storage & Analysis	Graph Database	Storage system for knowledge graph-based maps, enabling complex relationship querying.	Neo4j, Amazon Neptune [2]
Visualization & Dissemination	Evidence Synthesis Visualization Tool	Open-source software to generate interactive heatmaps, atlases, and charts from map databases.	EviAtlas R package [101]
Color Coding Guidance	Color Scheme Guidelines	Evidence-based principles for selecting colors in maps and visualizations to optimize discriminability and comprehension.	Use non-analogous, yellow-inclusive schemes; ensure high contrast [104].

Visualization Color Standards: Effective visual communication is paramount. Research indicates that for color-coded information in diagrams or heatmaps, non-analogous color schemes (mixing warm and cool colors) and schemes that include yellow significantly improve information-seeking performance and user preference [104]. Furthermore, to enhance the discriminability of nodes in network graphs (like knowledge graphs), using complementary colors for links (edges) relative to node colors is recommended, rather than using the same hue [105]. All visualizations must adhere to WCAG contrast guidelines (minimum 4.5:1 for normal text) to ensure accessibility [104].

In the evolving landscape of chemical and pharmaceutical risk management, the volume and complexity of scientific evidence present a fundamental challenge to regulators and developers. Traditional, narrative approaches to evidence synthesis are increasingly inadequate, risking bias, inconsistency, and a lack of transparency in critical decisions concerning public health and environmental safety [6]. Systematic Evidence Maps (SEMs) emerge as a powerful, resource-efficient tool designed to address this gap. Unlike a Systematic Review (SR), which provides a synthesized answer to a tightly focused question, an SEM offers a comprehensive, queryable overview of a broad evidence base [6]. This article details the application of SEMs within global risk management frameworks, providing the protocols and rationale for their integration as a precursor to targeted risk assessment and a mechanism for ongoing evidence surveillance, framed within a broader thesis on systematic evidence mapping for chemical risk management research.

Foundations of Systematic Evidence Mapping (SEM)

An SEM is defined as a systematically gathered database that characterizes key features of a research landscape. Its primary function is to catalogue and describe available evidence—such as the chemicals studied, health outcomes investigated, study designs employed, and model systems used—rather than to perform a quantitative synthesis of results [6]. This descriptive mapping allows for the identification of knowledge clusters and critical gaps, enabling more efficient prioritization for future systematic reviews or primary research.

The distinction between SEM and SR is foundational. The following table summarizes their contrasting characteristics and complementary roles within an evidence-based workflow.

Table 1: Comparative Analysis of Systematic Evidence Maps (SEM) and Systematic Reviews (SR)

Feature	Systematic Evidence Map (SEM)	Systematic Review (SR)
Primary Objective	To systematically catalogue and describe the breadth of an evidence base.	To answer a specific research question via synthesis and analysis of evidence.
Research Question	Broad, exploratory (e.g., "What evidence exists on the toxicological endpoints of chemical class X?").	Narrow, focused (e.g., "Does exposure to chemical Y increase the risk of outcome Z in population P?").
Output	Interactive database or structured report with evidence heatmaps; identifies knowledge clusters and gaps.	Qualitative and/or quantitative synthesis (e.g., meta-analysis) with a confidence assessment (e.g., GRADE).
Resource Intensity	Moderate to high (comprehensive searching/screening).	Very high (comprehensive searching, screening, extraction, critical appraisal, synthesis).
Key Role in Risk Management	Evidence Triage & Surveillance: Informs priority-setting, scopes future SRs, monitors emerging trends.	Decision Support: Provides synthesized effect estimates to directly inform risk assessment and permissible exposure levels [6].
Regulatory Utility	Efficiently manages large chemical portfolios (e.g., under TSCA, REACH); supports proactive, anticipatory regulation.	Provides the definitive scientific basis for risk determinations and risk management actions on prioritized substances.

Framework for Integrating SEMs into Global Risk Management

The integration of SEMs can enhance both prospective drug development and the retrospective evaluation of existing chemicals. The following diagrams illustrate the workflow for creating an SEM and its point of integration into a generalized chemical risk management lifecycle.

Diagram 1: Systematic Evidence Mapping Workflow (8 Key Steps) [6]

Diagram 2: SEM Integration in Chemical Risk Management Lifecycle

Application in Regulatory Contexts

SEMs align with the scientific and transparency mandates of modern regulations. For instance, the U.S. EPA's TSCA program requires risk evaluations to use the "best available science" and a "weight-of-scientific-evidence" approach [69]. An SEM conducted prior to a full risk evaluation ensures a transparent and defensible scoping phase, systematically identifying all relevant studies and endpoints, thereby strengthening the subsequent hazard and exposure assessments. Similarly, for pharmaceutical risk management, early integration of SEMs can inform development strategies. A survey of Summary Basis of Approval documents for similar therapies can identify potential regulatory concerns and shape more robust risk mitigation plans from Phase I onward [106].

Quantitative Impact and Efficiency Gains

The value of SEMs is demonstrated through comparative efficiency. For example, a regulatory body using SEMs to triage a portfolio of 100 substances can quickly identify the 15-20 with the most complex or problematic evidence bases, focusing intensive SR resources where they are most needed. This prevents the inefficient allocation of resources to substances with sparse or straightforward data. Evidence suggests that failure to address regulatory feedback early can lead to significant delays; one case study describes an 18-month clinical hold resulting from an overlooked issue, which could potentially be preempted by evidence-mapping-informed strategy [106].

Detailed Application Notes and Protocols

Protocol 1: SEM for Scoping a Chemical Risk Evaluation

This protocol aligns with the initial scoping phase of frameworks like the U.S. EPA TSCA Risk Evaluation process [69].

• Objective: To systematically identify and characterize the available scientific literature on a specified chemical substance to inform the scope, conceptual model, and analysis plan for a subsequent risk evaluation.
• Materials: DistillerSR or Rayyan software; access to bibliographic databases (PubMed, Embase, Web of Science, ToxLine); data extraction form.
• Procedure:
  • Define the Mapping Question: Formulate a broad question (e.g., "What is the extent and nature of evidence on the human health and ecological hazards of [Chemical X]?").
  • Develop Search Strategy: Collaborate with an information specialist. Use chemical identifiers (CAS RN, name variants) and broad health/environmental outcome terms. Apply no date or language filters initially. Document the strategy per PRISMA-S guidelines.
  • Screening:
    • Level 1 (Title/Abstract): Two independent reviewers screen records against inclusion criteria (e.g., primary research on [Chemical X] reporting a measured hazard or exposure outcome).
    • Level 2 (Full Text): Two reviewers assess the full text of potentially relevant studies. Conflicts are resolved by consensus or a third reviewer.
  • Data Extraction & Coding: Extract metadata into a standardized form. Key fields include: study identifier, publication year, study type (in vivo, in vitro, epidemiological), test system, exposure regime, primary outcome measures reported, and study funding source.
  • Evidence Database Construction: Populate a relational database or structured spreadsheet with extracted data to allow for filtering and querying.
  • Visualization & Reporting: Generate evidence "heatmaps" showing the volume of studies by outcome and study type. Write a report summarizing the evidence landscape, highlighting robustly studied endpoints, data gaps, and potential clusters of studies suitable for rapid or full systematic review.

Protocol 2: SEM for Proactive Drug Development Risk Planning

This protocol operationalizes risk management principles in early-stage drug development [106].

• Objective: To map the competitive and regulatory landscape for a therapeutic indication to anticipate development risks and inform the regulatory strategy plan.
• Materials: Regulatory database access (FDA Drug Approvals, EMA EPAR); commercial intelligence tools; database of published literature.
• Procedure:
  • Landscape Analysis: Conduct a systematic search for approved and investigational products within the target indication. Extract data on mechanism of action, trial endpoints, and labeled safety information.
  • Regulatory Precedent Mapping: Systematically retrieve and analyze publicly available regulatory documents (e.g., FDA Summary Basis of Approval, EMA Assessment Reports) for identified competitor products [106]. Code for key themes: specific nonclinical studies requested, clinical trial design elements, safety concerns raised by agencies, and required Risk Evaluation and Mitigation Strategies (REMS).
  • Evidence Gap Analysis: Compare the competitor landscape and regulatory feedback against the developer's proposed development plan. Visually map the alignment and discrepancies.
  • Risk Summary Grid Development: Synthesize findings into a dynamic risk grid. Categorize identified risks (e.g., "Need for specific cardiovascular safety study") by probability and impact. Link each risk to a proposed mitigation action (e.g., "Schedule Pre-IND meeting to discuss CV study design") and assign ownership [106].
  • Strategic Output: The final output is an evidence-informed regulatory strategy plan and a living risk mitigation log, which serves as a foundational document for internal decision-making and early regulatory engagement.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Tools for Conducting Systematic Evidence Maps

Tool Category	Specific Tool/Resource	Primary Function in SEM
Project Management	DistillerSR, Rayyan, Covidence	Screening & Data Extraction: Platforms that manage the systematic review workflow, enabling dual independent screening, conflict resolution, and form-based data extraction with high reproducibility.
Bibliographic Databases	PubMed/MEDLINE, Embase, Web of Science, Scopus, ToxLine	Comprehensive Searching: Provide access to the published biomedical, toxicological, and environmental science literature. Using multiple databases is critical to minimize retrieval bias.
Grey Literature Sources	Regulatory agency websites (FDA, EPA, ECHA), clinical trial registries (ClinicalTrials.gov), ProQuest Dissertations	Minimizing Publication Bias: Identifying unpublished studies, ongoing trials, and regulatory reports essential for a complete evidence picture.
Data Visualization	R (ggplot2, circlize), Python (matplotlib, seaborn), Tableau	Evidence Mapping: Generating heatmaps, bubble plots, and network diagrams to visually represent the distribution and relationships within the mapped evidence base.
Dynamic Documentation	Open Science Framework (OSF), Git-based repositories (GitHub, GitLab)	Protocol & Process Transparency: Hosting the pre-registered public protocol, search strategies, and data extraction forms to ensure full reproducibility and transparency.

The integration of Systematic Evidence Maps into global risk management frameworks represents a paradigm shift toward proactive, transparent, and efficient evidence-based decision-making. For chemical regulators under TSCA or REACH, SEMs offer a scalable solution to triage large chemical portfolios and ensure risk evaluations are grounded in a comprehensive understanding of the science. For drug developers, SEMs applied to the competitive and regulatory landscape provide a strategic tool to anticipate and mitigate risks, potentially averting costly delays [106]. As a foundational element of a broader thesis on systematic mapping, this approach underscores that effective risk management in the 21st century must begin not with answering a single question, but with first systematically understanding the entire map of evidence from which all answers must be derived [6].

Conclusion

Systematic evidence maps offer a powerful approach to navigating complex evidence landscapes in chemical risk management. By systematically cataloging and visualizing research, SEMs identify critical gaps, inform priority-setting, and support transparent decision-making[citation:1][citation:3]. Key takeaways include the importance of rigorous methodology, the value of interactive tools, and the need for standardization. Future directions should focus on enhancing automation through AI and machine learning, adopting knowledge graphs for data integration, and expanding SEMs into broader regulatory frameworks like EU REACH and US TSCA to accelerate drug development and environmental health assessments[citation:4][citation:7].

References

• 1. Systematic evidence maps as a novel tool to support ... [https://www.sciencedirect.com/science/article/pii/S0160412019310323]
• 2. A Survey of Systematic Evidence Mapping Practice and the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7261145/]
• 3. Systematic evidence map (SEM) template: Report format and ... [https://assessments.epa.gov/risk/document/&deid%3D364489]
• 4. Systematic Evidence Map (SEM) | Toxic Substance Portal [https://wwwn.cdc.gov/TSp/substances/SystematicEvidenceMap.aspx]
• 5. a systematic evidence map of modifying factors | Risk ... [https://assessments.epa.gov/risk/document/&deid%3D364165]
• 6. Systematic evidence maps as a novel tool to support ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7189619/]
• 7. Evidence Mapping: The Ultimate Guide [https://sprinto.com/blog/evidence-mapping/]
• 8. Protocol for a systematic evidence map of environmental ... [https://www.sciencedirect.com/science/article/pii/S0160412025005902]
• 9. A systematic review of the risk management frameworks for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10655695/]
• 10. Systematic review in chemical risk assessment [https://www.sciencedirect.com/science/article/abs/pii/S0160412015301124]
• 11. Systems-based analysis of the factors affecting the use ... [https://www.sciencedirect.com/science/article/pii/S0160412025006105]
• 12. Risk Matrix: Complete Guide With an Example [https://www.metricstream.com/learn/what-is-risk-matrix.html]
• 13. How to Use a Risk Assessment Matrix [https://www.standardfusion.com/blog/how-to-use-a-risk-assessment-matrix]
• 14. Safety Risk Assessment Matrix: The 2025 Guide for High- ... [https://field1st.com/safety-management/risk-assessment-matrix/]
• 15. Colors Of Safety: Your Guide To Color Codes And Meanings [https://www.safetydecals.com/blogs/news/colors-of-safety?srsltid=AfmBOorCrvXrIm2R7jt6V-vx1JrgvMCfgmYvNPF3ZJVzyFjkqp5TZJ4k]
• 16. Guidance to undertaking systematic evidence maps [https://www.sciencedirect.com/science/article/pii/S0160412025005781]
• 17. What is an evidence map? A systematic review of published ... [https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-016-0204-x]
• 18. Draft Protocol for Systematic Review in TSCA Risk ... [https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/draft-protocol-systematic-review-tsca-risk-evaluations]
• 19. Use of systematic evidence maps within the US ... [https://www.sciencedirect.com/science/article/pii/S0160412022002902]
• 20. Systematic evidence maps as a novel tool to support ... [https://research.lancaster-university.uk/en/publications/systematic-evidence-maps-as-a-novel-tool-to-support-evidence-base/publications/]
• 21. Systematic and scoping reviews: A comparison and overview [https://pubmed.ncbi.nlm.nih.gov/36414363/]
• 22. Systematic and scoping reviews: A comparison and overview [https://www.sciencedirect.com/science/article/abs/pii/S0895796722000631]
• 23. Application of systematic evidence mapping to assess the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7917575/]
• 24. The difference between a systematic review & scoping review [https://www.covidence.org/blog/the-difference-between-a-systematic-review-and-a-scoping-review/]
• 25. The methodology of quantitative risk assessment studies [https://pmc.ncbi.nlm.nih.gov/articles/PMC10821313/]
• 26. Scoping Review vs Systematic Review: Understanding The ... [https://www.laser.ai/blog/systematic-review-versus-scoping-review]
• 27. Development of a quantitative chemical risk assessment ... [https://www.sciencedirect.com/science/article/abs/pii/S0043135421001093]
• 28. A quantitative analysis method based on network evolution ... [https://www.nature.com/articles/s41598-025-88279-8]
• 29. Systematic evidence map (SEM) template [https://pmc.ncbi.nlm.nih.gov/articles/PMC12044641/]
• 30. New report calls for expanding the risk assessment toolbox [https://www.niehs.nih.gov/news/factor/2023/9/science-highlights/guidelines-for-nams-risk-assessment]
• 31. A call to action: Advancing new approach methodologies ... [https://www.sciencedirect.com/science/article/pii/S0273230025001345]
• 32. Identifying the PECO: A framework for formulating good ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6908441/]
• 33. Structure Data for Analysis [https://help.tableau.com/current/pro/desktop/en-us/data_structure_for_analysis.htm]
• 34. Implementing systematic review techniques in chemical risk ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4881816/]
• 35. SYSTEMATIC LITERATURE SEARCH METHODS ... - NCBI [https://www.ncbi.nlm.nih.gov/books/NBK601288/]
• 36. Database Search Strategies - Systematic Reviews [https://library.hmsom.edu/SystematicReviews/Searching]
• 37. Database Search Strategies - Systematic Reviews [https://musc.libguides.com/systematicreviews/searching]
• 38. Search Strategies - Systematic Reviews/Evidence Synthesis [https://libguides.adelphi.edu/Systematic_Reviews/search_strategies]
• 39. Proof-of-concept for using machine learning to facilitate ... [https://pubmed.ncbi.nlm.nih.gov/40256199/]
• 40. Interpretable machine learning for predicting key ... [https://www.sciencedirect.com/science/article/abs/pii/S0956053X25005227]
• 41. "A Machine Learning Approach to Improve Prediction in ... [https://arrow.tudublin.ie/saml/12/]
• 42. Frontiers | AI and ML-based risk assessment of chemicals : predicting... [https://www.frontiersin.org/journals/toxicology/articles/10.3389/ftox.2024.1461587/full]
• 43. Data Visualization: Tools - Research Guides [https://research.lib.buffalo.edu/dataviz/tools]
• 44. A machine learning driven automated system to extract multiple information fields from safety data sheet documents - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC11872447/]
• 45. Full length article Systematic evidence map (SEM) template [https://www.sciencedirect.com/science/article/pii/S0160412022003956]
• 46. Application of Feature Extraction Methods for Chemical ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8434006/]
• 47. Downloadable Computational Toxicology Data [https://www.epa.gov/comptox-tools/downloadable-computational-toxicology-data]
• 48. Understanding the effects of natural hazards on chemical emission incidents using machine learning techniques - ScienceDirect [https://www.sciencedirect.com/science/article/abs/pii/S0957582025001673?via=ihub]
• 49. EPA Begins Five Chemical Risk Evaluations Under Toxic ... [https://www.epa.gov/newsreleases/epa-begins-five-chemical-risk-evaluations-under-toxic-substances-control-act-starts]
• 50. Marketing Dashboards: The #1 Guide with 25+ Examples ... [https://improvado.io/blog/12-best-marketing-dashboard-examples-and-templates]
• 51. Text has minimum contrast[proposed] | ACT Rule | WAI [https://www.w3.org/WAI/standards-guidelines/act/rules/afw4f7/proposed/]
• 52. Color Contrast Accessibility: Complete WCAG 2025 Guide ... [https://www.allaccessible.org/blog/color-contrast-accessibility-wcag-guide-2025]
• 53. 34 Best Power BI Dashboard Examples & Templates in 2025 [https://blog.coupler.io/power-bi-dashboard-examples/]
• 54. Facilitation of risk assessment with evidence-based methods [https://www.sciencedirect.com/science/article/pii/S0273230020302166]
• 55. How to do a Quantitative Risk Assessment (QRA) - Blog [https://www.gexcon.com/resources/blog/how-to-do-a-quantitative-risk-assessment-qra/]
• 56. How to choose colors for data visualizations [https://www.atlassian.com/data/charts/how-to-choose-colors-data-visualization]
• 57. How to Choose Color Palettes for Graph and Data ... [https://mindthegraph.com/blog/how-to-choose-color-palettes-for-graph-and-data-visualization/]
• 58. Understanding Success Criterion 1.4.3: Contrast (Minimum) [https://www.w3.org/WAI/WCAG21/Understanding/contrast-minimum.html]
• 59. Elements must meet minimum color contrast ratio thresholds [https://dequeuniversity.com/rules/axe/4.8/color-contrast]
• 60. Building a knowledge graph for risk analysis of hazardous ... [https://www.sciencedirect.com/science/article/abs/pii/S100495412500415X]
• 61. Knowledge Graphs and Their Applications in Drug Discovery [https://pubmed.ncbi.nlm.nih.gov/37702941/]
• 62. Chemical Species Ontology for Data Integration and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10647085/]
• 63. HAZOP knowledge graph construction: Dual optimization ... [https://www.sciencedirect.com/science/article/abs/pii/S0950423025002724]
• 64. Conducting a Human Health Risk Assessment [https://www.epa.gov/risk/conducting-human-health-risk-assessment]
• 65. Five Applications of Knowledge Graphs in the Drug ... [https://altair.com/blog/articles/applications-of-knowledge-graphs-drug-discovery]
• 66. From Chaos to Control: Why Workflow Automation is ... [https://www.appliedinnovation.com/automation-services/from-chaos-to-control-why-workflow-automation-is-the-2025-must-have/]
• 67. AI Workflow Automation Trends for 2025 [https://www.cflowapps.com/ai-workflow-automation-trends/]
• 68. Standard, Guidelines, Safe Methods of Use (SMOUs) [https://www.auckland.ac.nz/en/health-safety-wellbeing/health-safety-topics/laboratory-safety/chemical-safety/lab-rules-and-safe-methods.html]
• 69. Risk Evaluations for Existing Chemicals under TSCA [https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/risk-evaluations-existing-chemicals-under-tsca]
• 70. The need for transparency and reproducibility in ... [https://www.sciencedirect.com/science/article/pii/S0273230016303002]
• 71. Using semantics to scale up evidence-based chemical risk ... [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260712]
• 72. Colors with Good Contrast | Web Accessibility Initiative (WAI) [https://www.w3.org/WAI/perspective-videos/contrast/]
• 73. 7 Tips for Evaluating Color Contrast for Maps That Enhance ... [https://www.maplibrary.org/10389/7-tips-for-evaluating-color-contrast-for-maps/]
• 74. Colour map displays - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12173982/]
• 75. Risk Reporting: Best Color Palettes for Data Visualization [https://onspring.com/risk-reporting-best-color-palettes-for-data-visualization/]
• 76. Evaluation of Qualitative Colour Palettes for Tactile Maps [https://www.mdpi.com/2220-9964/13/3/94]
• 77. Effective stakeholder engagement for decision-relevant ... [https://www.sciencedirect.com/science/article/pii/S1462901125000048]
• 78. Stakeholder Engagement and Outreach for Safer ... [https://www.epa.gov/chemical-research/stakeholder-engagement-and-outreach-safer-chemicals-research]
• 79. Mapping the Rise in Machine Learning in Environmental ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12567794/]
• 80. Quantitative Risk Assessment Method for Laboratory Safety [https://www.sciencedirect.com/science/article/abs/pii/S0950423025002931]
• 81. Chemical Safety Risk Identification and Analysis Based on ... [https://www.mdpi.com/2076-3417/15/11/6197]
• 82. Understanding different types of review articles: A primer for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12118787/]
• 83. A comprehensive guide to conduct a systematic review and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12366998/]
• 84. Systematic Review vs Meta Analysis: Key Differences (2025) [https://paperguide.ai/blog/systematic-review-vs-meta-analysis/]
• 85. Rethinking Systematic Review Assignment Design in ... [https://journals.indianapolis.iu.edu/index.php/hypothesis/article/view/28463]
• 86. Systematic maps and literature-driven AOP development [https://assessments.epa.gov/risk/document/&deid%3D352243]
• 87. Application of a Unified Probabilistic Framework to the Dose ... [https://assessments.epa.gov/risk/document/&deid%3D352392]
• 88. A First-Tier Framework for Assessing Toxicological Risk ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9782653/]
• 89. ACROLEIN | Occupational Safety and Health Administration [http://www.osha.gov/chemicaldata/51]
• 90. NIOSH Pocket Guide to Chemical Hazards - Acrolein [https://www.cdc.gov/niosh/npg/npgd0011.html]
• 91. Application of a unified probabilistic framework to the dose- ... [https://www.sciencedirect.com/science/article/pii/S0160412020319085]
• 92. Permissible Exposure Limits – OSHA Annotated Table Z-1 [http://www.osha.gov/annotated-pels/table-z-1]
• 93. Cannabis Users Face Substantially Higher Risk of Heart ... [https://www.acc.org/About-ACC/Press-Releases/2025/03/17/15/35/Cannabis-Users-Face-Substantially-Higher-Risk]
• 94. The Challenge of Risk Assessment ... [https://www.mdpi.com/2078-1547/13/2/32]
• 95. Systematic evidence maps as a novel tool to support evidence ... [https://pubmed.ncbi.nlm.nih.gov/31254867/]
• 96. Procedures for Chemical Risk Evaluation Under the Toxic ... [https://www.federalregister.gov/documents/2025/09/23/2025-18431/procedures-for-chemical-risk-evaluation-under-the-toxic-substances-control-act-tsca]
• 97. EPA Proposes Rollbacks to Chemical Regulation [https://www.theregreview.org/2025/10/28/bradley-epa-proposes-rollbacks-to-chemical-regulation/]
• 98. Risk Management Program (RMP) Rule [https://www.epa.gov/rmp/risk-management-program-safer-communities-chemical-accident-prevention-final-rule]
• 99. Principles of Effective Data Visualization - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC7733875/]
• 100. Guides - Data Visualization [https://infoguides.gmu.edu/data-visualization/guides]
• 101. EviAtlas: a tool for visualising evidence synthesis databases [https://environmentalevidencejournal.biomedcentral.com/articles/10.1186/s13750-019-0167-1]
• 102. EPA Proposes Changes to TSCA Risk Evaluation ... [https://www.bdlaw.com/publications/epa-proposes-changes-to-tsca-risk-evaluation-framework-rule-again/]
• 103. Search for reporting guidelines [https://www.equator-network.org/reporting-guidelines/]
• 104. What Color Scheme is More Effective in Assisting Readers ... [https://arxiv.org/html/2408.06494v2]
• 105. Discriminability of node colors in node-link diagrams and ... [https://www.sciencedirect.com/science/article/pii/S2468502X25000713]
• 106. A Practical Approach to Risk During Management Drug Development [https://globalforum.diaglobal.org/issue/january-2025/a-practical-approach-to-risk-management-during-drug-development/]