Prostate cancer surveillance by occupation and industry: the Canadian Census Health and Environment Cohort (CanCHEC)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As there are no well-established modifiable risk factors for prostate cancer, further evidence is needed on possible factors such as occupation. Our study uses one of the largest Canadian worker cohorts to examine occupation, industry, and prostate cancer and to assess patterns of prostate cancer rates. The Canadian Census Health and Environment Cohort (CanCHEC) was established by linking the 1991 Canadian Census Cohort to the Canadian Cancer Database (1969-2010), Canadian Mortality Database (1991-2011), and Tax Summary Files (1981-2011). A total of 37,695 prostate cancer cases were identified in men aged 25-74 based on age at diagnosis. Cox proportional hazards models were used to estimate hazards ratios and 95% confidence intervals. In men aged 25-74 years, elevated risks were observed in the following occupations: senior management (HR = 1.12, 95% CI: 1.04-1.20); office and administration (HR = 1.19, 95% CI: 1.11-1.27); finance services (HR = 1.09, 95% CI: 1.04-1.14); education (HR = 1.05, 95% CI: 1.00-1.11); agriculture and farm management (HR = 1.12, 95% CI: 1.06-1.17); farm work (HR = 1.11, 95% CI: 1.01-1.21); construction managers (HR = 1.07, 95% CI: 1.01-1.14); firefighting (HR = 1.17, 95% CI: 1.01-1.36); and police work (HR = 1.22, 95% CI: 1.09-1.36). Decreased risks were observed across other construction and transportation occupations. Results by industry were consistent with occupation results. Associations were identified for white-collar, agriculture, protective services, construction, and transportation occupations. These findings emphasize the need for further study of job-related exposures and the potential influence of nonoccupational factors such as screening practices.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it