Assessing the impact of the duration and intensity of inhalation exposure on the magnitude of the variability of internal dose metrics in children and adults
Bibliographic record
Abstract
The objective of this study was to assess the impact of the exposure duration and intensity on the human kinetic adjustment factor (HKAF). A physiologically based pharmacokinetic model was used to compute target dose metrics (i.e. maximum blood concentration (C(max)) and amount metabolized/L liver/24 h (Amet)) in adults, neonates (0-30 days), toddlers (1-3 years), and pregnant women following inhalation exposure to benzene, styrene, 1,1,1-trichloroethane and 1,4-dioxane. Exposure scenarios simulated involved various concentrations based on the chemical's reference concentration (low) and six of U.S. EPA's Acute Exposure Guideline Levels (AEGLs) (high), for durations of 10 min, 60 min, 8 h, and 24 h, as well as at steady-state. Distributions for body weight (BW), height (H), and hepatic CYP2E1 content were obtained from the literature or from P3M software, whereas blood flows and tissue volumes were calculated from BW and H. The HKAF was computed based on distributions of dose metrics obtained by Monte Carlo simulations [95th percentile in each subpopulation/median in adults]. At low levels of exposure, ranges of C(max)-based HKAF were 1-6.8 depending on the chemical, with 1,4-dioxane exhibiting the greatest values. At high levels of exposure, this range was 1.1-5.2, with styrene exhibiting the greatest value. Neonates were always the most sensitive subpopulation based on C(max), and pregnant women were most sensitive based on Amet in the majority of the cases (1.3-2.1). These results have shown that the chemical-specific HKAF varies as a function of exposure duration and intensity of inhalation exposures, and sometimes exceeds the default value used in risk assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".