The Association between Tea Consumption and Bladder Cancer Risk Based on the Bladder Cancer Epidemiology and Nutritional Determinants (BLEND) International Consortium
Bibliographic record
Abstract
OBJECTIVES: Evidence regarding the association between tea consumption and bladder cancer (BC) risk is inconsistent. This study aimed to increase our knowledge of the association by using international data from the Bladder Cancer Epidemiology and Nutritional Determinants Consortium. METHODS: Individual data on 2,347 cases and 6,871 controls from 15 case-control studies with information on black, green, herbal, or general tea was pooled. The association was estimated using multilevel multivariable logistic regression analysis adjusted for multiple (non-)dietary factors. RESULTS: Association between tea consumption and BC risk was observed (odds ratio, OR = 0.72, 95% confidence interval, 95% CI = 0.65-0.80) compared to non-tea drinkers. Stratified analyses based on gender and smoking status yielded similar results. Stratified analysis showed no significant association between black or green tea consumption and BC risk across models, while herbal tea consumption was linked to a reduced BC risk (OR = 0.59, 95% CI = 0.36-0.96). As daily tea consumption increased within a suitable range (<5.67 cups/day), BC risk decreased. CONCLUSIONS: tea showed no association with BC risk, while herbal tea was inversely linked to BC incidence. Despite some significant findings in the selected strata, further studies are required to clarify the underlying mechanisms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".