Surveillance of cancer risks for firefighters, police, and armed forces among men in a Canadian census cohort
Bibliographic record
Abstract
BACKGROUND: Firefighters, police, and armed services may be exposed to hazards such as combustion by-products and shift work. METHODS: The CanCHEC cohort linked 1991 census data to the Canadian cancer registry for follow up. Cox proportional hazards modeling was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) to estimate risks for firefighter, police, or armed forces compared to workers in other occupations. RESULTS: The cohort of 1 108 410 men included 4535 firefighters, 10 055 police, and 9165 armed forces. For firefighters, elevated risks were noted for Hodgkin's lymphoma (HR: 2.89, 95%CI: 1.29-6.46), melanoma (HR: 1.67, 95%CI: 1.17-2.37), and prostate cancer (HR: 1.18, 95%CI: 1.01-1.37). Police had elevated risks for melanoma (HR:1.69, 95%CI: 1.32-2.16) and prostate cancer (HR:1.28, 95%CI: 1.14-1.42). No significant associations were found for armed forces workers. CONCLUSIONS: Canadian firefighters, police, and armed services, may be at an increased risk of developing certain cancers. Results suggested that a healthy worker effect may influence risk estimates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".