Contribution of Toxicokinetic Modeling to the Adjustment of Exposure Limits to Unusual Work Schedules
Bibliographic record
Abstract
This study compared two toxicokinetic approaches for determining correction factors to be applied to occupational exposure limits (ELs) for unusual exposure scenarios: a classic one-compartment toxicokinetic approach and the physiologically based toxicokinetic (PBTK) approach. The approaches were applied to three typical unusual exposure scenarios: four consecutive 10-hour workdays followed by 3 days of recovery; three consecutive 12-hour workdays followed by 4 days of recovery; and a 4/3 work schedule. Results indicate that use of an adjustment method for ELs based on contaminant toxicokinetics generates less protective correction factors (i.e., a smaller adjustment) than those obtained using the U.S. Occupational Safety and Health Administration approach, which is based on Haber's law. Among all scenarios tested, the highest adjustment required, resulting from the use of a toxicokinetic approach (PBTK or one-compartment), was for the 4/3 work schedule and for a contaminant with a half-life equal to 18 hours. In that case the ELs would need to be reduced by 26%. Based on previous work, the authors believe an adjustment based on a toxicokinetic approach is more realistic from a toxicological standpoint. Given the value of a substance's half-life, the use of the graphs of Hickey and Reist (developed from a one-compartment toxicokinetic model) is a rapid and reliable means of establishing the correction factor. However, this approach is limited to simple and repetitive scenarios. For more complex exposure scenarios, such as that corresponding to a 4/3 work schedule, a one-compartment model also can be developed for each of the needs. Finally, the use of PBTK models allows greater flexibility for adjusting ELs for novel work schedules.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".