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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".