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Record W2563510133 · doi:10.1002/ieam.1883

Challenges in assigning endocrine-specific modes of action: Recommendations for researchers and regulators

2016· article· en· W2563510133 on OpenAlexaff
Ellen Mihaich, Christoph Schäfers, David A. Dreier, Markus Hecker, Lisa S. Ortego, Yukio Kawashima, Keith R. Solomon

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsUniversity of GuelphUniversity of SaskatchewanEnvironment and Climate Change Canada
Fundersnot available
KeywordsAction (physics)Endocrine systemRisk analysis (engineering)Computer scienceBusinessComputational biologyMedicineBiologyEndocrinologyHormone

Abstract

fetched live from OpenAlex

Abstract As regulatory programs evaluate substances for their endocrine-disrupting properties, careful study design and data interpretation are needed to distinguish between responses that are truly endocrine specific and those that are not. This is particularly important in regulatory environments where criteria are under development to identify endocrine-disrupting properties to enable hazard-based regulation. Irrespective of these processes, most jurisdictions use the World Health Organization/International Programme on Chemical Safety definition of an endocrine disruptor, requiring that a substance is demonstrated to cause a change in endocrine function that consequently leads to an adverse effect in an intact organism. Such a definition is broad, and at its most cautious might capture many general mechanisms that would not specifically denote an endocrine disruptor. In addition, endocrine responses may be adaptive in nature, designed to maintain homeostasis rather than induce an irreversible adverse effect. The likelihood of indirect effects is increased in (eco)toxicological studies that require the use of maximum tolerated concentrations or doses, which must produce some adverse effect. The misidentification of indirect effects as truly endocrine mediated has serious consequences for prompting animal- and resource-intensive testing and regulatory consequences. To minimize the risk for misidentification, an objective and transparent weight-of-evidence procedure based on biological plausibility, essentiality, and empirical evidence of key events in an adverse outcome pathway is recommended to describe the modes of action that may be involved in toxic responses in nontarget organisms. Confounding factors such as systemic toxicity, general stress, and infection can add complexity to such an evaluation and should be considered in the weight of evidence. A recommended set of questions is proffered to help guide researchers and regulators in discerning endocrine and nonendocrine responses. Although many examples provided in this study are based on ecotoxicology, the majority of the concepts and processes are applicable to both environmental and human health assessments. Integr Environ Assess Manag 2017;13:280–292. © 2016 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) Key Points An objective and transparent weight-of-evidence procedure based on biological plausibility, essentiality, and empirical evidence of key events in an adverse outcome pathway is recommended to describe the modes of action that may be involved in toxic responses in nontarget organisms. Mechanistically linking key events along a proposed endocrine-specific adverse outcome pathway is the first step in establishing biological plausibility for a suspected endocrine-active substance to cause adverse apical effects. Examples are given on how systemic toxicity, infection, general stress, and nutritional status can confound, limit, or influence the assessment of whether adverse effects are a consequence of an endocrine mode of action. Questions were developed to help guide researchers and regulators in the evaluation process to resolve whether a chemical has an endocrine-specific mode of action resulting in adverse effects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.371
metaresearch head score (Gemma)0.437
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.371
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3710.437
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0090.011
Bibliometrics0.0100.010
Science and technology studies0.0080.020
Scholarly communication0.0220.040
Open science0.0250.016
Research integrity0.0460.049
Insufficient payload (model declined to judge)0.0150.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.098
GPT teacher head0.400
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2016
Admission routes1
Has abstractyes

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