Prostate cancer risk by occupation in the Occupational Disease Surveillance System (ODSS) in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Previous Canadian epidemiologic studies have identified associations between occupations and prostate cancer risk, though evidence is limited. However, there are no well-established preventable risk factors for prostate cancer, which warrants the need for further investigation into occupational factors to strengthen existing evidence. This study uses occupation and prostate cancer information from a large surveillance cohort in Ontario that linked workers' compensation claim data to administrative health databases. METHODS: Occupations were examined using the Occupational Disease Surveillance System (ODSS). ODSS included 1 231 177 male workers for the 1983 to 2015 period, whose records were linked to the Ontario Cancer Registry (OCR) in order to identify and follow up on prostate cancer diagnoses. Cox proportional hazard models were used to calculate age-adjusted hazard ratios and 95% CI to estimate the risk of prostate cancer by occupation group. RESULTS: A total of 34 997 prostate cancer cases were diagnosed among workers in ODSS. Overall, elevated prostate cancer risk was observed for men employed in management/ administration (HR 2.17, 95% CI = 1.98-2.38), teaching (HR 1.99, 95% CI = 1.79-2.21), transportation (HR 1.20, 95% CI = 1.16-1.24), construction (HR 1.09, 95% CI = 1.06-1.12), firefighting (HR 1.62, 95% CI = 1.47-1.78), and police work (HR 1.20, 95% CI = 1.10-1.32). Inconsistent findings were observed for clerical and farming occupations. CONCLUSION: Associations observed in white collar, construction, transportation, and protective services occupations were consistent with previous Canadian studies. Findings emphasize the need to assess job-specific exposures, sedentary behaviour, psychological stress, and shift work. Understanding specific occupational risk factors can lead to better understanding of prostate cancer etiology and improve prevention strategies.
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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 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".