Dietary fat and breast cancer risk revisited: a meta-analysis of the published literature
Bibliographic record
Abstract
Animal experiments and human ecological studies suggest that dietary fat intake is associated with a risk of breast cancer, but individual-based studies have given contradictory results. We have carried out a meta-analysis of this association to include all papers published up to July 2003. Case-control and cohort studies that examined the association of dietary fat, or fat-containing foods, with risk of breast cancer were identified. A total of 45 risk estimates for total fat intake were obtained. Descriptive data from each study were extracted with an estimate of relative risk and its associated 95% confidence interval (CI), and were analysed using the random effects model of DerSimonian and Laird. The summary relative risk, comparing the highest and lowest levels of intake of total fat, was 1.13 (95% CI: 1.03-1.25). Cohort studies (N=14) had a summary relative risk of 1.11 (95% CI: 0.99-1.25) and case-control studies (N=31) had a relative risk of 1.14 (95% CI 0.99-1.32). Significant summary relative risks were also found for saturated fat (RR, 1.19; 95% CI: 1.06-1.35) and meat intake (RR, 1.17; 95% CI 1.06-1.29). Combined estimates of risk for total and saturated fat intake, and for meat intake, all indicate an association between higher intakes and an increased risk of breast cancer. Case-control and cohort studies gave similar results.
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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 | 0.037 | 0.079 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.060 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".