O-115 IDENTIFICATION OF THE MOST PREVALENT TOXIC EFFECT CLASSES OF AIRBORNE CHEMICALS MEASURED DURING INDUSTRIAL HYGIENE REGULATORY INSPECTIONS IN US WORKPLACES
Bibliographic record
Abstract
Abstract Introduction Occupational exposure to multiple chemicals (multiexposures) is more frequent than exceptional. This project explores how multiexposures can be translated into potential toxic risks to workers’ health Methods Measurement data collected by U.S Occupational Safety and Health Administration (OSHA) inspectors were linked to the MiXie database, which identifies classes of toxic effects (n=24) for > 700 chemicals. The unit of analysis, a workplace situation (WS), corresponded to measurements taken for the same job title within a company within a year. Depending on the agents measured and detected, the relevant toxic effect classes from MiXie were attributed to each WS. Across all WSs, we identified the most prevalent classes and evaluated associations between classes using clustering approaches. Results The OSHA database included 340,837 personal detected measurements of 206 airborne chemicals from 123,964 WSs collected between 1971-2021. WSs had a median of 2 (1-218) measurements and 5 (1-20) toxic effect classes. The five toxic effect classes associated with the most WSs were Carcinogenicity and/or mutagenicity (60% of measurements), Central nervous system damage (58%), Lower airway damage (54%), Upper airway damage (53%), and Ocular damage (49%). Airway damage (upper/lower), Ocular damage and Skin damage classes were clustered. Discussion Although the OSHA database data is not representative of all occupational multiexposure situations in the US, its objectively-measured exposures allow the exploration of potential toxic effects in workers. Conclusion Our approach provides some insight into the additive toxic effects associated with multiexposures to frequently measured airborne chemicals in the US.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".