0339 Occupation and risk of prostate cancer in a national population-based cohort study: the canadian census health and environment cohort
Bibliographic record
Abstract
Prostate cancer is one of the most commonly diagnosed cancers in men and further evidence is needed on preventable risk factors. This study investigated the relationship between prostate cancer risk and occupation using a large Canadian cohort. The Canadian Census Health and Environment Cohort was established by linking the 1991 Canadian Census Cohort to the Canadian Cancer Database (1969–2010), Canadian Mortality Database (1991–2011) and the Tax Summary Files (1981–2011). A total of 37 695 prostate cancer cases were identified based on age at diagnosis. Cox proportional hazards models were used to estimate hazards ratios (HR) and 95% confidence intervals (CI). Overall, age standardised prostate cancer rates were observed to be highest in white collar workers and lowest in construction/transportation workers. In men aged 50–74 years, elevated risks were observed in agriculture management (HR=1.11, 95% CI 1.06–1.17), farm work (HR=1.12, 95% CI 1.02–1.23), firefighting (HR=1.16, 95% CI 1.00–1.35), military (HR=1.14, 95% CI 1.00–1.32), police (HR=1.28, 95% CI 1.14–1.42), senior management (HR=1.09, 95% CI 1.02–1.17), office (HR=1.16, 95% CI 1.08–1.24), and finance (HR=1.08, 95% CI 1.03–1.13). Similar findings were observed in men aged 25–49 years, with additional elevated risks in office management (HR=1.19, 95% CI 1.11–1.27) and education (HR=1.05, 95% CI 1.00–1.11). Decreased risks were observed in construction and transportation occupations in both age groups. Findings across agriculture and protective services were consistent with previous studies. Some findings, particularly among management occupations, may be due to screening. Further investigation is needed on job-specific exposures with better understanding on differences in rates across occupations.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".