P193 Occupation as a predictor of prostate cancer screening behaviour
Bibliographic record
Abstract
Objectives Prostate cancer (PCa) is the most common malignancy in Canadian men. Frequently, it has minimal health impacts and can go undetected for years. Therefore the detection of PCa is impacted by screening practices that are often related to lifestyle and occupational factors, making it difficult to evaluate their etiologic role. Therefore, we examined potential occupational differences in the likelihood to undergo PCa screening. Methods Data were from the Prostate Cancer & Environment Study case-control study. Detailed demographic, lifestyle, screening and occupational information was available for 1,994 population-based controls from Montreal, Canada (we excluded cases since all had been screened). We used the longest job held, categorised into 16 groups. Using logistic regression, we modelled the odds of having ever been screened for PCa by job category. We also explored these associations for having a family history of PCa, being a regular drinker, being a regular smoker, marital status, ancestry, education, income, physical activity at work and leisure, age, and body mass index. Results A total of 1933 controls with complete data were included; 1746 (90%) had ever been screened and 187 (10%) had not. As expected, screening was associated with family history of PCa (OR 2.6; 95% CI: 1.2–5.6), being married (OR 2.0; CI: 1.4–2.8), being older (p < 0.0001), and ancestry (OR for Asian men compared to European: 0.4; 95% CI: 0.2–0.9). Income and education were not significantly associated with screening in the adjusted model. However, compared to men working in management, those working in clerical jobs, primary industries, fabrication, construction and transportation were less likely to have been screened. Conclusion We found considerable variation in screening behaviours for PCa across different jobs. Future studies assessing the role of occupation in PCa risk should take screening into consideration when investigating occupational exposures as etiologic factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".