A Retrospective Performance Assessment of the Developmental Neurotoxicity Study in Support of OECD Test Guideline 426
Bibliographic record
Abstract
OBJECTIVE: We conducted a review of the history and performance of developmental neurotoxicity (DNT) testing in support of the finalization and implementation of Organisation of Economic Co-operation and Development (OECD) DNT test guideline 426 (TG 426). INFORMATION SOURCES AND ANALYSIS: In this review we summarize extensive scientific efforts that form the foundation for this testing paradigm, including basic neurotoxicology research, interlaboratory collaborative studies, expert workshops, and validation studies, and we address the relevance, applicability, and use of the DNT study in risk assessment. CONCLUSIONS: The OECD DNT guideline represents the best available science for assessing the potential for DNT in human health risk assessment, and data generated with this protocol are relevant and reliable for the assessment of these end points. The test methods used have been subjected to an extensive history of international validation, peer review, and evaluation, which is contained in the public record. The reproducibility, reliability, and sensitivity of these methods have been demonstrated, using a wide variety of test substances, in accordance with OECD guidance on the validation and international acceptance of new or updated test methods for hazard characterization. Multiple independent, expert scientific peer reviews affirm these conclusions.
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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 | 0.047 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".