Coffee and tea consumption and risk of prostate cancer in the European Prospective Investigation into Cancer and Nutrition
Bibliographic record
Abstract
The epidemiological evidence regarding the association of coffee and tea consumption with prostate cancer risk is inconclusive, and few cohort studies have assessed these associations by disease stage and grade. We examined the associations of coffee (total, caffeinated and decaffeinated) and tea intake with prostate cancer risk in the European Prospective Investigation into Cancer and Nutrition. Among 142,196 men, 7,036 incident prostate cancer cases were diagnosed over 14 years of follow-up. Data on coffee and tea consumption were collected through validated country-specific food questionnaires at baseline. We used Cox proportional hazards regression models to compute hazard ratios (HRs) and 95% confidence intervals (CI). Models were stratified by center and age, and adjusted for anthropometric, lifestyle and dietary factors. Median coffee and tea intake were 375 and 106 mL/day, respectively, but large variations existed by country. Comparing the highest (median of 855 mL/day) versus lowest (median of 103 mL/day) consumers of coffee and tea (450 vs. 12 mL/day) the HRs were 1.02 (95% CI, 0.94-1.09) and 0.98 (95% CI, 0.90-1.07) for risk of total prostate cancer and 0.97 (95% CI, 0.79-1.21) and 0.89 (95% CI, 0.70-1.13) for risk of fatal disease, respectively. No evidence of association was seen for consumption of total, caffeinated or decaffeinated coffee or tea and risk of total prostate cancer or cancer by stage, grade or fatality in this large cohort. Further investigations are needed to clarify whether an association exists by different preparations or by concentrations and constituents of these beverages.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".