Statistical Modeling of Occupational Exposure to Polycyclic Aromatic Hydrocarbons Using OSHA Data
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are a group of pollutants with multiple variants classified as carcinogenic. The Occupational Safety and Health Administration (OSHA) provided access to two PAH exposure databanks of United States workplace compliance testing data collected between 1979 and 2010. Mixed-effects logistic models were used to predict the exceedance fraction (EF), i.e., the probability of exceeding OSHA's Permissible Exposure Limit (PEL = 0.2 mg/m3) for PAHs based on industry and occupation. Measurements of coal tar pitch volatiles were used as a surrogate for PAHs. Time, databank, occupation, and industry were included as fixed-effects while an identifier for the compliance inspection number was included as a random effect. Analyses involved 2,509 full-shift personal measurements. Results showed that the majority of industries had an estimated EF < 0.5, although several industries, including Standardized Industry Classification codes 1623 (Water, Sewer, Pipeline, and Communication and Powerline Construction), 1711 (Plumbing, Heating, and Air-Conditioning), 2824 (Manmade Organic Fibres), 3496 (Misc. Fabricated Wire products), and 5812 (Eating Places), and Major group's 13 (Oil and Gas Extraction) and 30 (Rubber and Miscellaneous Plastic Products), were estimated to have more than an 80% likelihood of exceeding the PEL. There was an inverse temporal trend of exceeding the PEL, with lower risk in most recent years, albeit not statistically significant. Similar results were shown when incorporating occupation, but varied depending on the occupation as the majority of industries predicted at the administrative level, e.g., managers, had an estimated EF < 0.5 while at the minimally skilled/laborer level there was a substantial increase in the estimated EF. These statistical models allow the prediction of PAH exposure risk through individual occupational histories and will be used to create a job-exposure matrix for use in a population-based case-control study exploring PAH exposure and breast cancer risk.
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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 | 0.001 |
| 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".