Exposure to welding fumes increases lung cancer risk among light smokers but not among heavy smokers: evidence from two case–control studies in <scp>M</scp>ontreal
Bibliographic record
Abstract
We investigated relationships between occupational exposure to gas and arc welding fumes and the risk of lung cancer among workers exposed to these agents throughout the spectrum of industries. Two population-based case-control studies were conducted in Montreal. Study I (1979-1986) included 857 cases and 1066 controls, and Study II (1996-2001) comprised 736 cases and 894 controls. Detailed job histories were obtained by interview and evaluated by an expert team of chemist-hygienists to estimate degree of exposure to approximately 300 substances for each job. Gas and arc welding fumes were among the agents evaluated. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) of lung cancer using logistic regression, adjusting for smoking history and other covariates. The two studies provided similar results, so a pooled analysis was conducted. Among all subjects, no significant association was found between lung cancer and gas welding fumes (OR = 1.1; 95% CI = 0.9-1.4) or arc welding fumes (OR = 1.0; 95% CI = 0.8-1.2). However, when restricting attention to light smokers, there was an increased risk of lung cancer in relation to gas welding fumes (OR = 2.9; 95% CI = 1.7-4.8) and arc welding fumes (OR = 2.3; 95% CI = 1.3-3.8), with even higher OR estimates among workers with the highest cumulative exposures. In conclusion, there was no detectable excess risk of lung cancer due to welding fumes among moderate to heavy smokers; but among light smokers we found an excess risk related to both types of welding fumes.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".