Application of the New Asthma-Specific Job Exposure Matrix: A Study in Quebec Apprentice Cohort Exposed to Isocyanates
Bibliographic record
Abstract
Background: Recently, the first asthma-specific Job Exposure Matrix (JEM) was updated to occupational asthma-specific JEM (OAsJEM). Our study aimed to evaluate the association between continued exposure to isocyanates and incident work-related chest symptoms in former car-painting apprentices and to compare the associations using the first and new OAsJEMs.Methods: We used data from an inception cohort of male car-painting apprentices. Post-apprenticeship exposure to isocyanate during follow-up was evaluated using the first asthma-specific JEM (“exposed”=1 or “not exposed”=0) and the new OAsJEM (high=2, medium=1, and none=0). Association between occupation exposure to isocyanate and incidence of work-related rhinoconjunctival and chest symptoms were evaluated through cox regression models, adjusted for age, smoking, wheezing, and non-specific bronchial hyperresponsiveness. Results: The agreement between the two JEMs (exposed vs non-exposed to isocyanate) was perfect (kappa coefficient=0.946, p<0.001). There were only five subjects who were classified as non-exposed based on the first JEM, but had a medium exposure to isocyanate based on the new OAsJEM. Exposure to isocyanate increased the risk of occupational chest symptoms in the high-exposure category (hazard ratio [HR] 2.7, 95% CI 1.1 – 6.6) and the medium category (HR 2.9, 95% CI 0.3 – 30.0) compared to the reference group based on the new OAsJEM, whereas an HR of 2.5 (95% CI 1.0-6.2) was observed from the first JEM. Both JEMs yielded an inconclusive association between exposure to isocyanates and the risk for work-related rhino-conjunctivitis. Conclusion: The asthma-specific JEM and OAsJEM consistently showed that isocyanate exposure increased the risk of incident work-related chest symptoms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".