Natural resource-based industries and prostate cancer risk in Northeastern Ontario: a case–control study
Bibliographic record
Abstract
OBJECTIVE: Prostate cancer continues to be the most commonly diagnosed cancer in men, and there is limited knowledge on its preventable risk factors. A number of occupational exposures in natural resource-based industries are suspected to be related to prostate cancer risk. This study investigates associations between employment in these industries and prostate cancer. METHODS: Data were from a population-based, case-control study previously conducted in Northeastern Ontario. Incident cases (N=760) aged 45-85 years and diagnosed with prostate cancer between 1995 and 1998 were identified from the Ontario Cancer Registry. Controls (N=1632) were recruited using telephone listings, and were frequency matched to cases by age. Lifetime occupational history was collected for all participants. Logistic regression was used to estimate ORs and their associated 95% CIs. RESULTS: Elevated risks were observed for employment in forestry and logging industries (OR=1.87, 95% CI 1.32 to 2.73) and occupations (OR=1.71, 95% CI 1.24 to 2.35), and these risks increased with duration of employment for ≥10 years. Elevated risks were also found for employment in wood products industries (OR=1.45, 95% CI 1.07 to 1.97), and paper and allied products industries (OR=1.43, 95% CI 1.03 to 2.00), and when duration of employment was ≥10 years. There were also elevated risks in agriculture and mining-related work; however, these findings were not consistent across industry and occupation categories. CONCLUSIONS: Prostate cancer risk may be associated with work in several natural resource industries, primarily in the forest industries. To further evaluate observed associations, studies should focus on natural resource-based exposures in larger populations with improved exposure assessment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".