O-321 Exploration of occupations as risk factors for lung cancer in multiple exposure hierarchical and penalization models
Bibliographic record
Abstract
Introduction We used hierarchical and penalization models to explore occupational risks associated with lung cancer while accounting for exposures to multiple known carcinogenic exposures. Methods We pooled lung cancer case-control study subjects from 14 European and Canadian studies. Associations between employment in 1,506 five-digit ISCO-68 occupations and lung cancer were screened using Bayesian hierarchical and lasso penalized regressions accounting for age, smoking, sex, study, and fully quantitative exposures to six known occupational lung carcinogens: asbestos, chromium, diesel engine exhaust, nickel, PAHs, and silica. False positive findings in the penalization model were controlled using stability selection with specified family-wise error rates. Lung cancer odds ratios for selected occupations were calculated using unconditional logistic regression model with identical covariates. Results Our study included 16,901 cases and 20,965 controls. Jobs selected by the hierarchical and penalization models were similar. Occupations with positive associations with lung cancer after controlling for the known carcinogens included building painters (OR: 1.40; 95 CI: 1.17, 1.67), carpenters (OR: 1.77; 95 CI: 1.36, 2.33), and paviours (OR: 3.91; 95 CI: 1.75, 9.61). Conclusion We demonstrated viable agnostic approaches in identifying employment risk factors for lung cancer. Future work involves investigations of factors that contribute to the observed elevated cancer risks.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".