Intakes of coffee, tea, milk, soda and juice and renal cell cancer in a pooled analysis of 13 prospective studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 it