Intakes of coffee, tea, milk, soda and juice and renal cell cancer in a pooled analysis of 13 prospective studies
Bibliographic record
Abstract
Specific beverage intake may be associated with the risk of renal cell cancer through a diluting effect of carcinogens, alterations of hormone levels, or other changes in the renal tubular environment, but few prospective studies have examined these associations. We evaluated the associations between coffee, tea, milk, soda and fruit and vegetable juice intakes and renal cell cancer risk in a pooled analysis of 13 prospective studies (530,469 women and 244,483 men). Participants completed a validated food-frequency questionnaire at baseline. Using the primary data, the study-specific relative risks (RRs) were calculated and then pooled using a random effects model. A total of 1,478 incident renal cell cancer cases were identified during a follow-up of 7-20 years across studies. Coffee consumption was associated with a modestly lower risk of renal cell cancer (pooled multivariate RR for 3 or more 8 oz (237 ml) cups/day versus less than one 8 oz (237 ml) cup/day = 0.84; 95% CI = 0.67-1.05; p value, test for trend = 0.22). Tea consumption was also inversely associated with renal cell cancer risk (pooled multivariate RR for 1 or more 8 oz (237 ml) cups/day versus nondrinkers = 0.85; 95% CI = 0.71-1.02; pvalue, test for trend = 0.04). No clear associations were observed for milk, soda or juice. Our findings provide strong evidence that neither coffee nor tea consumption increases renal cell cancer risk. Instead, greater consumption of coffee and tea may be associated with a lower risk of renal cell cancer. (c) 2007 Wiley-Liss, Inc.
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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 | 0.029 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".