Chemical selection for the Thyroid Validation Study coordinated by EURL ECVAM and involving EU-NETVAL laboratories_suppl2
Bibliographic record
Abstract
The aim of the Thyroid Validation Study, coordinated by EURL ECVAM and involving EU-NETVAL laboratories, was to validate selected non-animal methods for the identification of chemicals that can potentially disrupt the thyroid hormone system in humans. The validation study was organized in two parts: Part 1 was to assess method performance and develop standard operating procedures, where needed, and Part 2 was to assess the mechanistic relevance of the methods using a set of validation chemicals. This paper describes the stepwise process to select this validation set of chemicals, mainly based on extensive literature review and expert judgment elicitation to identify chemicals for which there was evidence to show their (lack of) ability to perturb the thyroid hormone signaling mechanisms or modes of action covered by the methods. A unique contribution of the study lies in its mechanistic coverage of molecular targets within the thyroid gland but also regulatory mechanisms in peripheral tissues, reflecting a multifaceted perspective on thyroid hormone action. The validation set consisted of 30 chemicals, providing a balanced representation across a broad chemical space and offering insights into the mechanistic relevance of the selected methods. Once validated, these methods will contribute to advancing the identification and evaluation of endocrine disruptors, informing regulatory decisions, and promoting alternative testing strategies. Plain language summaryThis manuscript provides important insights that will allow the progression of non-animal methods for thyroid hormone disruption and their use in the regulatory context. The selection of chemicals used in a validation study is of paramount importance and will impact on the overall performance of the methods being validated. The selection approach used is described in detail and provides a relevant and useful guide for possible future validation studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.046 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".