Association between lifetime alcohol consumption and prostate cancer risk: A case-control study in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Alcohol intake may increase the risk of prostate cancer (PCa). Many previous studies harbored important methodological limitations. METHODS: We conducted a population-based case-control study of PCa comprising 1933 cases and 1994 controls in Montreal, Canada. Lifetime alcohol consumption was elicited, by type of beverage, during in-person interviews. Odds ratios (OR) and 95% confidence intervals (CI) assessed the association between alcohol intake and PCa risk, adjusting for potential confounders and considering the subjects' PCa screening history. RESULTS: We observed a weak, non-significant positive association between high consumption of total alcohol over the lifetime and risk of high-grade PCa (OR=1.18, 95% CI 0.81-1.73). Risk estimates were more pronounced among current drinkers (OR=1.40, 95%CI 1.00-1.97), particularly after adjusting for the timing of last PCa screening (OR=1.52, 95%CI 1.07-2.16). These associations were largely driven by beer consumption. The OR for high-grade PCa associated with high beer intake was 1.37 (95%CI 1.00-1.89); it was 1.49 (95%CI 0.99-2.23) among current drinkers and 1.68 (95% CI 1.10-2.57) after adjusting for screening recency. High cumulative consumption of spirits was associated with a lower risk of low-grade PCa (OR=0.75, 95%CI 0.60-0.94) but the risk estimate no longer achieved statistical significance when restricting to current users. No association was found for wine consumption. CONCLUSION: Findings add to the accumulating evidence that high alcohol consumption increases the risk of high-grade PCa. This association largely reflected beer intake in our population, and was strengthened when taking into account PCa screening history.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".