Intake of Fruits and Vegetables and Risk of Breast Cancer
Bibliographic record
Abstract
CONTEXT: Some epidemiologic studies suggest that elevated fruit and vegetable consumption is associated with a reduced risk of breast cancer. However, most have been case-control studies in which recall and selection bias may influence the results. Additionally, publication bias may have influenced the literature on associations for specific fruit and vegetable subgroups. OBJECTIVE: To examine the association between breast cancer and total and specific fruit and vegetable group intakes using standardized exposure definitions. DATA SOURCES/STUDY SELECTION: Eight prospective studies that had at least 200 incident breast cancer cases, assessed usual dietary intake, and completed a validation study of the diet assessment method or a closely related instrument were included in these analyses. DATA EXTRACTION: Using the primary data from each of the studies, we calculated study-specific relative risks (RRs) that were combined using a random-effects model. DATA SYNTHESIS: The studies included 7377 incident invasive breast cancer cases occurring among 351 825 women whose diet was analyzed at baseline. For comparisons of the highest vs lowest quartiles of intake, weak, nonsignificant associations were observed for total fruits (pooled multivariate RR, 0.93; 95% confidence interval [CI], 0.86-1.00; P for trend =.08), total vegetables (RR, 0.96; 95% CI, 0.89-1.04; P for trend =.54), and total fruits and vegetables (RR, 0.93; 95% CI, 0.86-1.00; P for trend =.12). No additional benefit was apparent in comparisons of the highest and lowest deciles of intake. No associations were observed for green leafy vegetables, 8 botanical groups, and 17 specific fruits and vegetables. CONCLUSION: These results suggest that fruit and vegetable consumption during adulthood is not significantly associated with reduced breast cancer 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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".