O-060 EXPLORING SEX DIFFERENCES IN LUNG CANCER RISK AMONG A LARGE COHORT OF ONTARIO, CANADA WORKERS
Bibliographic record
Abstract
Abstract Background While occupational exposures are known to contribute to lung cancer risk, there is less known about sex differences, specifically regarding the risk among female workers. This study aimed to explore sex differences in lung cancer risk across occupational groups, with a focus on female workers. Methods A cohort of approximately 2.37 million workers with lost-time compensation claims were linked to the Ontario Cancer Registry and followed until lung cancer diagnosis, age 85, emigration, death, or end of follow-up (Dec 31, 2020). Cox proportional hazards models were used to estimate sex-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for lung cancer by occupational group, adjusted for birth-year and age and indirectly adjusted for cigarette smoking. Results A total of 12,216 and 30,291 incident lung cancer cases were identified among females and males, respectively. Several occupations demonstrated stronger associations for lung cancer in females, with a more than 20% increased risk compared to males. These occupations at the major level include mining and quarrying (HR=3.05, 95%CI=1.37-6.78); materials processing (chemical, petroleum, rubber, plastic) (HR=1.35, 95%CI=1.19-1.52); wood processing (HR=1.87, 95%CI=1.22-2.87); metal machining (HR=1.56, 95%CI=1.21-2.00); metal shaping and forming (HR=1.46, 95%CI=1.32-1.62); mechanic and repair work (HR=1.39, 95% CI=1.04-1.85); and printing (HR=1.51, 95%CI=1.30-1.75). Discussion and conclusion Findings demonstrate a stronger association of lung cancer risk in blue collar occupations among female workers when compared to male workers. Findings may be due to sex differences in various factors that impact risk, such as occupational exposures, use and effectiveness of personal protective equipment, and other biological/lifestyle factors.
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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) | 0.002 | 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 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".