276 Occupation, industry, and the risk of prostate cancer: a case-control study in Montréal, Canada
Bibliographic record
Abstract
Introduction Prostate cancer (PCa) is the most common cancer among Canadian men. Age, a family history of PCa and ancestry are the only recognised risk factors, but a role for environmental influences is suspected. Objective To explore the relationship between occupational and industry titles, and PCa risk. Methods PROtEuS (Prostate cancer & Environment Study) is a case-control study of PCa in Montréal, Canada including some 2000 cases and 2000 population controls aged 40–75 years. Detailed occupational histories were elicited through in-person interviews. Unconditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between PCa and employment in 98 occupations and 74 industries. Models were adjusted for age, ancestry, family history of PCa, PCa screening, income and physical activity. Results Elevated PCa risks were found for social sciences workers (OR 1.9; 95% CI 1.1–3.4) and metal product fabricators (OR 3.4; 95%CI 1.3–9.3) employed ≥ 10 years, and for non-construction painters (OR 3.4; 95% CI 1.3–9.0) and plumbers and steamfitters (OR 2.4; 95%CI 1.1–5.4) employed < 10 years. Reduced risks were observed for farmers (OR 0.6; 95%CI 0.4–0.9) and food services workers (OR 0.6; 95% CI 0.4–0.9) employed < 10 years, and for physical scientists employed ≥ 10 years (OR 0.6; 95% CI 0.5–0.9). Workers in public service, wood products, and jewellery industries had excess PCa risks. Those in agriculture, miscellaneous food, accommodation and food, air transport, and retail trade industries had reduced risks. Analyses including all subjects, or stratified according to white or blue collar status generated consistent findings. Conclusions Our findings suggest excess PCa risks in some metal-related occupations, as well as in white-collar occupations such as in public service and social sciences. Farmers and food services workers had reduced risks. Specific occupational exposures possibly underlying these associations will be explored.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".