Occupational exposures and lung cancer risk - an analysis of the CARTaGENE study
Bibliographic record
Abstract
Objective: To investigate the possible associations between selected occupational agents and lung cancer risk. Methods: A case-cohort design was nested within the CARTaGENE study. Cases included all participants with an incident diagnosis of lung cancer occurring during the follow-up from 2009 to 2015 (N=178). For comparison, a sub-cohort of 1033 individuals was established based on a stratified sample of the cohort at baseline. Information on participants’ longest-held job was collected at baseline and coded by an occupational hygienist according to the International Standard Classification of Occupations 1968 (ISCO-68). The job codes were then linked to the Canadian Job Exposure Matrix (CANJEM) to determine the probability of exposure to a list of 258 agents. This analysis was restricted to the 28 most prevalent agents with at least 5 exposed cases. Separate multivariable logistic regression models with robust variance estimators were used to estimate odd ratios (OR) and 95% confidence intervals (95% CI) for the associations between each agent and lung cancer risk while controlling for established lung cancer risk factors, notably smoking. Results: Increased lung cancer risk was found among those exposed to ashes (OR=3.8; 95% CI: 1.5-9.5), hydrogen chloride (OR=4.4; 95% CI: 1.2-16.0), formaldehyde (OR=2.3; 95% CI: 1.3-4.2), cooking fumes (OR=2.4; 95% CI: 1.1-5.3), paints and varnishes used on surfaces other than metal and wood (OR=3.2; 95% CI: 1.0-9.8), alkanes (OR=2.6; 95% CI: 1.2-5.5), aliphatic aldehydes (OR= 2.9; 95% CI: 1.3-4.3), and cleaning agents (OR=1.6; 95% CI: 1.0-2.5). A reduced lung cancer risk was observed among participants exposed to gasoline engine emissions (OR=0.5; 95% CI: 0.2-1.0) and polycyclic aromatic hydrocarbons (PAHs) from petroleum (OR=0.3; 95% CI: 0.1-0.9). Conclusion: Our preliminary findings provide support for the role of several occupational agents, for which we have limited knowledge, in contributing to lung cancer risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".