RF-291 Long working hours and prostate cancer risk: results from a population-based case-control study
Bibliographic record
Abstract
Introduction Recent evidence suggests that long work hours have a detrimental effect on health, possibly through unhealthy lifestyle behaviours, stress, diet and sleep disturbances. Hardly any research has evaluated whether long work hours relate to cancer incidence. Objectives To investigate the association between long work hours and prostate cancer risk, overall and by cancer aggressiveness. Methods We used data from the Prostate Cancer & Environment Study, a case-control study conducted in Montreal, Canada, in 2005–2012. In all, 1,931 incident cases, aged < 7 6, were ascertained across hospitals in 2005–2009 and 1,994 age-matched (± 5 years) controls were randomly selected from the electoral list. Detailed descriptions of each job held for ≥ 2 years, including working hours, were elicited through in-person interviews. Long work hours were defined as working time exceeding the Canadian standard of 40 hours/week. Unconditional logistic regression was used to derive odds ratio (ORs) and 95% confidence intervals (CIs) for the association between long work hours and prostate cancer risk, adjusted for age, education and ancestry. We also investigated whether associations varied according to a history of work at night. Results Overall, 2,477 subjects (64.6%) reported ever working > 40 hours/week. Occupations entailing long hours were most often related to management, administration and sales. Ever exposure to long work hours was associated with an OR of prostate cancer of 1.21 (95%CI 1.06–1.39), while it was of 1.50 (95%CI 1.23–1.83) for a cumulative duration of 11–23 years of long work hours. The ORs for aggressive cancer among men who engaged in long work hours and who had a history of work at night were 1.40 (95%CI 0.85–2.30), and 1.20 (95%CI 0.91–1.57) among those without such a history. Conclusion Findings suggest that long work hours, especially among men with a history of work at night, may influence prostate cancer risk.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".