Coffee consumption and risk of incident gout in men: A prospective study
Bibliographic record
Abstract
OBJECTIVE: Coffee is one of the most widely consumed beverages in the world and may affect the risk of gout via various mechanisms. We prospectively evaluated the relationship between coffee intake and the risk of incident gout in a large cohort of men. METHODS: Over a 12-year period, we studied 45,869 men with no history of gout at baseline. Intake of coffee, decaffeinated coffee, tea, and total caffeine was assessed every 4 years through validated questionnaires. We used a supplementary questionnaire to ascertain whether participants met the American College of Rheumatology survey criteria for gout. RESULTS: We documented 757 confirmed incident cases of gout. Increasing coffee intake was inversely associated with the risk of gout. The multivariate relative risks (RRs) for incident gout according to coffee consumption categories (0, <1, 1-3, 4-5, and > or = 6 cups per day) were 1.00, 0.97, 0.92, 0.60 (95% confidence interval [95% CI] 0.41-0.87), and 0.41 (95% CI 0.19-0.88), respectively (P for trend = 0.009). For decaffeinated coffee, the multivariate RRs according to consumption categories (0, <1, 1-3, and > or = 4 cups per day) were 1.00, 0.83, 0.67 (95% CI 0.54-0.82), and 0.73 (95% CI 0.46-1.17), respectively (P for trend = 0.002). Total caffeine from all sources and tea intake were not associated with the risk of gout. CONCLUSION: These prospective data suggest that long-term coffee consumption is associated with a lower risk of incident gout.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".