LIFECOURSE ANALYSIS OF MANUAL OCCUPATIONS AND RISK OF PROSTATE CANCER IN MONTREAL, CANADA: A CASE-CONTROL STUDY
Bibliographic record
Abstract
Introduction Cancer risk factors accumulate over time or affect the body at specific moments of development. Our lifecourse approach identified occupational exposures and social determinants of health associated with the risk of prostate cancer across a lifetime. Objectives To assess the risk of prostate cancer among men employed in manual occupations compared to the risk of those in non-manual occupations. Using three time points of exposure, we tested the accumulation, critical period and trajectory models. Methods Histologically-confirmed prostate cancer cases (n=1229), aged 40 to 75, were identified across 11 French hospitals in Montreal. Population controls (n=1307) were identified from the French provincial electoral list in the same areas of residence as the cases, and frequency-matched to cases by age (± 5 years). Information on socio-demographic, lifestyle characteristics, and detailed occupational history, was collected through face-to-face interviews. Using the British Registrar General's classification of occupations, jobs held at age 25 and 55, and father's occupation at birth, were categorised as manual or non-manual. Logistic regression models were applied. Results Men with exposure to manual occupations at the three time points were more likely to have had an unfavourable financial situation during childhood, a lower education level and a lower family income at diagnosis or interview. Based on the accumulation model, men who held only manual occupations were at 23% excess risk of prostate cancer [OR=1.23, 95%CI 1.00–1.51] compared to those who did not after controlling for other socioeconomic factors. Using the trajectory model, a decreased risk of prostate cancer [OR=0.79, 95%CI 0.59–1.05] was observed among men moving from manual to non-manual occupations during adulthood. The critical period model did not identify any risk differences when exposures occurred specifically at one of the three time points. Conclusion Although life-course models are difficult to disentangle, the observed associations between manual occupations and prostate cancer risk are consistent with both accumulation and trajectory models. Highest risks were observed with prolonged exposure to manual occupations. Conversely, moving from manual to non-manual occupations appeared to be protective against prostate cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".