Evolution of the Use of Toxicological Data for Evaluating Chemical Safety and Occupational Exposure Limits
Bibliographic record
Abstract
Abstract Conventional approaches to setting occupational exposure limits (OELs) by regulatory agencies are reviewed. Example OEL derivations show how guidance values are developed in practice. OEL values from various agencies may differ. We present a framework to help select the most appropriate OELs in practice. Use of the framework is demonstrated by OELs for n ‐hexane, a data‐rich chemical and methamphetamine, a data‐poor chemical. Looking forward, “New Approach Methodologies” (NAMs) continue to develop generating chemical toxicological data via high‐throughput in vitro screening assays, computational toxicology, and other methods. NAMs for chemical risk assessment have received support from organizations such as the Organization for Economic Cooperation and Development (OECD), which has validated a number of alternative test methods. Seven NAMs case studies for chemical screening and priority‐setting are presented. Future applications of NAMs in toxicological risk assessment and occupational environments are reviewed using a four‐level tiered testing framework previously proposed by Andersen and colleagues. The use of NAMs provides increased throughput, improved mechanistic understanding in toxicological testing and reduced costs compared to traditional mammalian toxicity testing. The formal application of NAMs in setting OELs is an area of ongoing development, and more widespread application of NAMs in deriving OELs is expected in the future.
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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 | 0.069 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".