Correlation of Occupational Exposure and Risk of Genitourinary Malignancies: A Canadian Population Study
Bibliographic record
Abstract
Objectives: Examining the impact of occupational exposure on malignancy is important in risk stratifying individual patients. To this end, our analyses focus on explaining the industrial effects on bladder, kidney and prostate cancer. Methods: Data were obtained from a population-based linked dataset, Canadian Census Health and Environment Cohort. Approximately 2.7 million people aged 25 or older who responded to the 1991 long-form Census questionnaire were linked with the Canadian Cancer Registry. Inclusion criteria included diagnosis between June 4, 1991, and December 31, 2010. Cox Proportional Hazards models were used to predict incidences of cancer with the agricultural industry as a reference point. Sex-specific analyses were then carried out. Results: Bladder cancer was diagnosed in 6970 men and 1665 women. The real estate industry was associated with increased risk for both sexes (men: hazard ratio [HR] 1.34; 95% confidence interval [CI]: 1.12-1.62, p<0.01; women: HR 1.63; 95% CI: 1.12-2.37, p< 0.05). Kidney cancer incidence was 4380 men and 1900 women. Health and social service was the only industry with increased risk for both men (HR 1.30; 95% CI: 1.04-1.62, p<0.05) and women (HR 1.36; 95% CI: 1.01-1.83, p<0.05). Prostate cancer incidence was 35220 men. A number of industries had a lower risk of prostate cancer diagnoses, such as accommodation and food (HR 0.77; 95% CI: 0.70-0.84, p<0.0001), when compared to the agriculture industry. Conclusions: Multivariate analysis, controlling for socioeconomic factors, found effects of real estate industry and health and social service consistently for both sexes in bladder and kidney cancer diagnosis, respectively. Prostate cancer incidence was highest in men from the agriculture industry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".