Green tea consumption and risk of breast cancer
Bibliographic record
Abstract
BACKGROUND: As the most popular beverage in East Asia, green tea (GT) has various biological activities effects such as anti-mutation, anti-oxidation, and anti-tumor. In this study, we aimed to evaluate whether GT consumption could be an effective way to decrease the risk of breast cancer. METHODS: We had performed a systematic review and updated meta-analysis of published case-control studies to evaluate the association between GT intake and the risk of breast cancer. Searching strategies were performed by the following keywords "Breast cancer," "breast neoplasm," and "green tea," with derivations and different combinations. The following databases were searched: PubMed, Cochrane Library, EMBASE, Web of science, China National Knowledge Infrastructure, WanFang, and China Biology Medicine disc. Studies published in both English and Chinese were considered for inclusion. Risk of bias was assessed through the Newcastle-Ottawa Scale (NOS). All data were analyzed through using Review Manager 5.1 software. RESULTS: Fourteen studies fulfilled inclusion criteria for meta-analysis, yielding a total of 14,058 breast cancer patients and 15,043 control subjects. Individuals with the habit of drinking GT were found to have a negative association with the risk of future breast cancer (odds ratio 0.83; 95% confidence interval: 0.72-0.96) despite significant heterogeneity. In subgroup analyses, the negative correlation was still found in studies using registry-based controls, NOS grades ≥6 and the number of cases <500. CONCLUSIONS: GT consumption may have a decreased incidence of breast cancer despite significant heterogeneity. However, owing to the quality of available studies, more properly designed trials are warranted to clarify the association between GT consumption and breast cancer.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".