Markov chain Monte Carlo simulation of biomonitoring in humans: application to biomarkers of chronic exposure to alkyl benzenes in the environment
Why this work is in the frame
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Bibliographic record
Abstract
Bayesian approaches are relevant for characterizing the population distribution of pharmacokinetic determinants as well as the exposure biomarkers of chemicals in the environment. The objective of this study was to conduct Bayesian analysis of the blood and alveolar air concentrations of alkyl benzenes (toluene, m-xylene and ethylbenzene) in humans chronically exposed to these chemicals in air. At steady-state, the blood and alveolar concentrations of alkyl benzenes are influenced by alveolar ventilation rate (QP), blood: air partition coefficient (PB), liver blood flow (QL) and intrinsic clearance (CLint). The prior information on these input parameters was obtained from the literature. The mean and variability of steady-state blood concentrations observed in a human volunteer study (n=4) was used as a basis to create a distribution (normal) from which samples (n = 16 and n = 50) were drawn using Monte Carlo approach. After Markov Chain Monte Carlo (MCMC) simulation with n = 16 (trial 1) and n = 50 (trail 2), posterior estimates of model parameters were obtained. The second updating of model parameters (trial 2) did not have an impact on the outcome. In general, the calculated steady-state biomarker concentrations compared well with the individual and population values. Overall, this study has demonstrated the feasibility of conducting MCMC simulations of human biomonitoring data, particularly during data-poor situations.
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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 it