Alcohol Intake and Renal Cell Cancer in a Pooled Analysis of 12 Prospective Studies
Bibliographic record
Abstract
BACKGROUND: The association between alcohol intake and risk of renal cell cancer has been inconsistent in case-control studies. An inverse association between alcohol intake and risk of renal cell cancer has been suggested in a few prospective studies, but each of these studies included a small number of cases. METHODS: We performed a pooled analysis of 12 prospective studies that included 530,469 women and 229,575 men with maximum follow-up times of 7-20 years. All participants had completed a validated food-frequency questionnaire at baseline. Using the primary data from each study, the study-specific relative risks (RRs) for renal cell cancer were calculated using Cox proportional hazards models and then pooled using a random-effects model. All statistical tests were two-sided. RESULTS: A total of 1430 (711 women and 719 men) cases of incident renal cell cancer were identified. The study-standardized incidence rates of renal cell cancer were 23 per 100,000 person-years among nondrinkers and 15 per 100,000 person-years among those who drank 15 g/day or more of alcohol. Compared with nondrinking, alcohol consumption (> or = 15 g/day, equivalent to slightly more than one alcoholic drink per day) was associated with a decreased risk of renal cell cancer (pooled multivariable RR = 0.72, 95% confidence interval = 0.60 to 0.86; P(trend)<.001); statistically significant inverse trends with increasing intake were seen in both women and men. No difference by sex was observed (P(heterogeneity) = .89). Associations between alcohol intake and renal cell cancer were not statistically different across alcoholic beverage type (beer versus wine versus liquor) (P = .40). CONCLUSION: Moderate alcohol consumption was associated with a lower risk of renal cell cancer among both women and men in this pooled analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".