Main issues addressed in the 2014–2015 revisions to the OECD Genetic Toxicology Test Guidelines
Bibliographic record
Abstract
The Organization for Economic Cooperation and Development (OECD) recently revised the test guidelines (TGs) for genetic toxicology. This article describes the main issues addressed during the revision process, and the new and consistent recommendations made in the revised TGs for: (1) demonstration of laboratory proficiency; (2) generation and use of robust historical control data; (3) improvement of the statistical power of the tests; (4) selection of top concentration for in vitro assays; (5) consistent data interpretation and determination of whether the result is clearly positive, clearly negative or needs closer consideration; and, (6) consideration of 3R's for in vivo assay design. The revision process resulted in improved consistency among OECD TGs (including the newly developed ones) and more comprehensive recommendations for the conduct and the interpretation of the assays. Altogether, the recommendations made during the revision process should improve the efficiency, by which the data are generated, and the quality and reliability of test results. Environ. Mol. Mutagen. 58:284-295, 2017. © 2017 Wiley Periodicals, Inc.
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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 | 0.084 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".