Exposure to occupational and domestic pesticides, and prostate cancer risk: preliminary findings from a case-control study in Montreal, Canada.
Bibliographic record
Abstract
Objectives To report on the association between pesticides exposure and prostate cancer (PC) risk from PROTEuS (Prostate Cancer & Environment Study), a case-control study on-going in Montreal. Methods Incident, histologically confirmed cases aged ≤75 were ascertained across major hospitals in Greater Montreal. Population controls from the same area, frequency-matched on age, were identified from electoral lists. This analysis is based on the first 820 cases and 661 controls interviewed to date. Subjects provided a detailed description of each job held over their lifetime; potential exposure to pesticides was assigned using the expert-based approach. Use of domestic pesticides was elicited. ORs and 95% CI were assessed, adjusting for age, family history of PC, body mass index and ethnicity. Results Exposure to pesticides was often seasonal. Pesticides exposure was most often assigned to farmers, carpenters and cooks. The OR for men with probable or definite occupational exposure to pesticides was 0.79 (95% CI 0.51 to 1.22). Domestic exposure to pesticides was associated with an OR of 0.83 (95% CI 0.66 to 1.05). Regular golfers had an OR of 0.75 (95% CI 0.59 to 0.96). Restricting analyses to controls who reported recent PC screening, or to cases with aggressive PC, yielded similar results. Conclusions We observed no association between pesticides exposure and PC cancer risk. However, the prevalence of exposure to pesticides was low (7%), limiting the ability to detect associations. As the study progresses towards its aim of 2000 cases and 2000 controls, results will be updated.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".