The association between green tea consumption and breast cancer risk: A systematic review and meta‐analysis
Bibliographic record
Abstract
This systematic review and meta‐analysis aimed to critically evaluate the relation between green tea (GT) consumption and the risk of breast cancer. Popular electronic databases were systematically searched for papers in English language. All case‐control and cohort studies in addition to randomized clinical trials were included if they assessed the chemopreventive effects of GT on breast cancer. The quality of included studies was assessed using the Newcastle–Ottawa and Jadad scale. This systematic review comprised 14 studies: 9 case‐control studies, 4 cohort studies, and 1 clinical trial. Odds ratio (OR) in case‐control studies suggested that women in the group receiving the highest level of GT had 19% reduction in breast cancer risk compared with those who received the lowest level of GT (summary OR = 0.81, p = .031; 95% CI [0.66, 0.981]; heterogeneity, I2 = 71.53, p < .001, random effect model; 9 studies). OR in cohort studies also showed no significant difference (OR = 0.99, p = .94; 95% CI [0.81, 1.138]; heterogeneity, I2 = 19.06, p = .29; fixed‐effect model; 4 studies). According to the only clinical trial, treatment with GT could not alter the mammographic density compared with placebo (26% vs. 25%). It cannot be concluded that GT consumption may decrease the risk of breast cancer. Due to high heterogeneity, a pooled analysis of case‐control and cohort studies was not performed.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".