Meta-analysis of studies on breast cancer risk and diet in Chinese women.
Bibliographic record
Abstract
OBJECTIVE: A meta-analysis was carried out to summarize published data on the relationship between breast cancer and dietary factors. METHODS: Databases in Chinese (China National Knowledge Infrastructure [CNKI], China Biology Medicine [CBM], WanFang, VIP) and in English (PubMed and Web of Science) were searched for articles analyzing vegetable, fruit, soy food and fat consumption and breast cancer risk published through June 30, 2013. Random effects models were used to estimate summary odds ratios (OR) based on high versus low intake, and subgroup analysis was conducted according to region, study design, paper quality and adjustment for confounding factors to detect the potential source of heterogeneity. Every study was screened according to the inclusion criteria and exclusion criteria, evaluated in accordance with the Newcastle-Ottawa Scale. RevMan 5.2 software was used for analysis. RESULTS: Of 785 studies retrieved, 22 met inclusion criteria (13 in Chinese and 9 in English), representing 23,201 patients: 10,566 in the experimental group and 12,635 in the control group. Thirteen included studies showed vegetables consumption to be a relevant factor in breast cancer risk, OR = 0.77 (95% CI [confidence interval] 0.62-0.96). Eleven studies showed fruits consumption to be relevant, OR = 0.68 (95% CI 0.49-0.93). Significant differences were also found between those who consumed soy foods, OR = 0.68 (95% CI 0.50-0.93) and those who ate a high-fat diet, OR = 1.15 (95% CI 1.01-1.30). CONCLUSION: This analysis confirms the association between intake of vegetables, fruits, soy foods and fat and the risk of breast cancer from published sources. It's suggested that high consumption of vegetables, fruits and soy foods may reduce the risk of breast cancer, while increasing fat consumption may increase the risk.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.070 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".