The Effects of Green Tea Consumption on Incidence of Breast Cancer and Recurrence of Breast Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Green tea is widely used by women for the prevention and treatment of breast cancer. The authors aimed to determine the efficacy of green tea ingestion on the risk of breast cancer development and the risk of breast cancer recurrence. METHODS: The authors conducted a systematic review and meta-analyses of observational studies from systematic searches of 8 electronic data sources and contact with authors. They included studies assessing breast cancer incidence and recurrence. RESULTS: Results: The pooled relative risk (RR) of developing breast cancer for the highest levels of green tea consumption in cohort studies was 0.89 (95% confidence interval [CI], 0.71-1.1; P= .28; I(2)= 0%), and in case control studies, the odds ratio was 0.44 (95% CI, 0.14-1.31; P= .14; I(2)= 47%). The pooled RR of cohort studies for breast cancer recurrence in all stages was 0.75 (95% CI, 0.47-1.19; P= .22; I(2)= 37%). A subgroup analysis of recurrence in stage I and II disease showed a pooled RR in cohort studies of 0.56 (95% CI, 0.38-0.83; P= .004; I2= 0%). Dose-response relationships were evident in only 3 of the 7 studies. CONCLUSION: To date, the epidemiological data indicates that consumption of 5 or more cups of green tea a day shows a non-statistically significant trend towards the prevention of breast cancer development. Evidence indicates that green tea consumption may possibly help prevent breast cancer recurrence in early stage (I and II) cancers. However, conclusions as to the potential therapeutic application of green tea are currently impossible to make due to the small number of studies conducted, the lack of any clinical trial evidence, the lack of a consistent dose-response relationship, and the potential for interaction with standard care.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".