Risk of chronic obstructive pulmonary disease in a large cohort of Ontario, Canada workers
Bibliographic record
Abstract
Abstract Although several occupational exposures have been linked to the risk of COPD; limited data exists on sex-specific differences. This study aimed to identify at-risk occupations and sex differences for COPD risk. Cases were identified in a large surveillance system established through the linkage of former compensation claimants’ data (non-COPD claims) to physician visits, ambulatory care data, and hospital inpatient data (1983–2020). Cox proportional hazard models were used to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CI) for occupation groups (occupation at time of claim), stratified by sex. HRs were indirectly adjusted for cigarette smoking using another population dataset. A total of 29,445 male and 14,693 female incident cases of COPD were identified. Increased risks were observed in both sexes for construction (HRmale 1.15, 95% CI 1.12–1.19; HRfemale 1.54, 95% CI 1.29–1.83) transport/equipment operating (HRmale 1.32, 95% CI 1.28–1.37; HRfemale 1.53, 95% CI 1.40–1.68) farming (HRmale 1.23, 95% CI 1.15–1.32; HRfemale 1.19, 95% CI 1.04–1.37) and janitors/cleaners (HRmale 1.31, 95% CI 1.24–1.37; HRfemale 1.40, 95% CI 1.31–1.49). Increased risks were observed for females employed as chefs and cooks (HR 1.44, 95% CI 1.31–1.58), bartenders (HR 1.38, 95% CI 1.05–1.81), and those working in food/beverage preparation (HR 1.34, 95% CI 1.24–1.45) among other occupations. This study demonstrates elevated risk of COPD among both male and female workers potentially exposed to vapours, gases, dusts, and fumes, highlighting the need for occupational surveillance of COPD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".