Tea Consumption, Alcohol Drinking and Physical Activity Associations with Breast Cancer Risk among Chinese Females: a Systematic Review and Meta-analysis
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
OBJECTIVE: To evaluate associations between tea consumption, alcohol drinking and physical activity and breast cancer risk among Chinese females. METHODS: Three English databases (PubMed, ScienceDirect and Wiley) and three Chinese databases (CNKI, WanFang and VIP) were independently searched by 2 reviewers up to December 2012, complemented by manual searches. The quality of included studies was assessed with the Newcastle-Ottawa Scale items. Random-effects models were used to estimate the pooled odds ratios (ORs) and 95% confidence intervals (CIs). Potential publication bias was estimated through Egger's and Begg's tests. Heterogeneity between studies was evaluated with I2 statistics. RESULTS: Thirty-nine studies involving 13,204 breast cancer cases and 87,248 controls were identified. Compared with non-drinkers, regular tea drinkers had decreased risk (OR=0.79, 95%CIs: 0.65-0.95; I2=84.9%; N=16). An inverse association was also found between regular physical activity and breast cancer risk (OR=0.73, 95%CIs: 0.63-0.85; I2=77.3%; N=15). However, there was no significant association between alcohol drinking and breast cancer risk (OR=0.85, 95%CIs: 0.72- 1.02; I2=63.8%; N=26). Most of the results from the subgroup analysis were consistent with the main results. CONCLUSION: Tea consumption and physical activity are significantly associated with a decreased risk of breast cancer in Chinese females. However, alcohol drinking may not be associated with any elevation of risk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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