Framework for human health risk assessment of non‐cancer effects resulting from short‐duration and intermittent exposures to chemicals
Bibliographic record
Abstract
Durations of exposure to chemicals, whether for single, repeated or intermittent periods, may vary from those upon which most guidance values are normally based. Because it is presently not feasible to conduct toxicity studies or develop toxicity reference values (TRVs) specific to each scenario of interest, methods are needed to address these various durations, drawing as much as possible on existing TRVs. A working framework was developed to address the potential for non-cancer effects resulting from continuous short-duration and intermittent exposures to chemicals. The framework presents an integrated, tiered approach that assists the user in identifying when existing TRVs can be applied directly, and the adaptations needed to assess the acceptability of short-duration or intermittent exposure scenarios. Descriptions of when and how toxicokinetic and toxicodynamic aspects need to be taken into consideration are also presented. The framework incorporates the use of TRVs based on exposure periods as similar as possible to the "actual" exposure periods and application of dose averaging under limited, specified conditions. This framework has been developed to aid in improving the scientific basis for the evaluation of short-duration and intermittent exposures in a variety of settings. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".