Evaluation of the Benchmark Dose for Point of Departure Determination for a Variety of Chemical Classes in Applied Regulatory Settings
Bibliographic record
Abstract
Repeated-dose studies received by the New Substances Assessment and Control Bureau (NSACB) of Health Canada are used to provide hazard information toward risk calculation. These studies provide a point of departure (POD), traditionally the NOAEL or LOAEL, which is used to extrapolate the quantity of substance above which adverse effects can be expected in humans. This project explored the use of benchmark dose (BMD) modeling as an alternative to this approach for studies with few dose groups. Continuous data from oral repeated-dose studies for chemicals previously assessed by NSACB were reanalyzed using U.S. EPA benchmark dose software (BMDS) to determine the BMD and BMD 95% lower confidence limit (BMDL(05) ) for each endpoint critical to NOAEL or LOAEL determination for each chemical. Endpoint-specific benchmark dose-response levels , indicative of adversity, were consistently applied. An overall BMD and BMDL(05) were calculated for each chemical using the geometric mean. The POD obtained from benchmark analysis was then compared with the traditional toxicity thresholds originally used for risk assessment. The BMD and BMDL(05) generally were higher than the NOAEL, but lower than the LOAEL. BMDL(05) was generally constant at 57% of the BMD. Benchmark provided a clear advantage in health risk assessment when a LOAEL was the only POD identified, or when dose groups were widely distributed. Although the benchmark method cannot always be applied, in the selected studies with few dose groups it provided a more accurate estimate of the real no-adverse-effect level of a substance.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".