An examination of the association between lifetime history of prostate and pancreatic cancer diagnosis and occupation in a population sample of Canadians
Bibliographic record
Abstract
BACKGROUND: Occupation was assessed as possible risk factors for prostate (PCa) and pancreatic cancer in a large Canadian worker cohort. METHODS: The Canadian Census Health and Environment Cohort (CanCHEC) was derived from linking the 1991 Canadian Census Cohort to the Canadian Cancer Database (1969-2010), Canadian Mortality Database (1991-2011), and Tax Summary Files (1981-2011). From the total sample of 1,931,110 persons, we identified and derived two samples of 28,610 men and 3,220 men and women with a past history of PCa and pancreatic cancer diagnoses, respectively. Cox proportional hazards models were used to estimate hazards ratios and 95% confidence intervals for occupation. RESULTS: In Canadian men aged 24-64 years, the highest elevated risks of PCa were observed for library clerks (HR = 2.36, 95% CI:1.12-4.97), medical radiation technologists (HR = 1.66, 95% CI:1.04-2.65), telecommunications and line cable workers (HR = 1.62, 95% CI: 1.22-3.16) and commissioned police officers (HR = 1.54, 95% CI: 1.10-2.16. The highest elevated risk for pancreatic cancer were observed for commissioned police officers (HR = 4.34, 95% CI: 1.85-10.21), photographic and film processors (HR = 3.97, 95% CI:1.69-9.34), railway and motor transport labourers (HR = 3.94, 95% CI: 1.67-9.29), and computer engineers (HR = 3.82, 95%CI: 1.52-9.61). CONCLUSION: These findings emphasize the need for further study of job-related exposures and the potential influence of non-occupational factors such as screening practices.
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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 | 0.004 |
| 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".