Exposures in painting-related occupations and risk of lung cancer among men: results from two case–control studies in Montreal
Bibliographic record
Abstract
BACKGROUND: Evidence that painters may be at risk for lung cancer comes mainly from analyses on job titles rather than on specific exposures found in the environments of painters. METHODS: In the context of two large population-based case-control studies of lung cancer carried out in Montreal, we were able to assess possible relationships between lung cancer and the occupation of painter as well as exposure to paints, varnishes and stains. Interviews for study I were conducted in 1979-1986 (857 cases, 533 population controls, 1349 cancer controls) and interviews for study II were conducted in 1996-2001 (765 cases and 899 controls). Detailed lifetime job histories were elicited; a team of hygienists and chemists evaluated the evidence of exposure to many occupational substances including paint-related substances. The relative risk of lung cancer was estimated, adjusting for several potential confounders, including smoking, in a three-variable parameterisation. RESULTS: In analyses pooling the two studies, painters had an OR of lung cancer of 1.3 (95% CI 0.9 to 2.2). Regarding exposures, ORs were: for wood varnishes and stains, 1.6 (95% CI 1.0 to 2.3); for wood and gypsum paints, 1.3 (95% CI 0.9 to 1.7); and for metal coatings, 1.1 (95% CI 0.8 to 1.6). Small numbers hampered evaluation of dose-response relationships. CONCLUSIONS: While our results cannot exclude chance or residual confounding by smoking or concomitant occupational exposures, they provide further evidence that some exposures in paint-related occupations, most notably wood varnishes and stains, increase the risk of lung cancer.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".