A Randomized Trial of Dietary Intervention for Breast Cancer Prevention
Bibliographic record
Abstract
Epidemiologic data and animal experiments suggest that dietary fat may influence risk of breast cancer. To determine whether intervention with a low-fat, high-carbohydrate diet would reduce breast cancer incidence in women at increased risk of the disease, we carried out a randomized controlled trial in Canada. We recruited 4,690 women with extensive mammographic density and randomized them to an intervention group or a comparison group. The intervention group received intensive dietary counseling to reduce fat intake to a target of 15% of calories and increase carbohydrate to 65% of calories. Dietary intakes were assessed throughout using food records. Subjects were followed for at least 7 years and for an average of 10 years. The main outcome was invasive breast cancer. Percentage of calories from fat in the intervention group decreased from 30% at baseline to 20% after randomization and remained 9% to 10% lower than the comparison group throughout. There were 118 invasive breast cancers in the intervention group and 102 in the comparison group [adjusted hazard ratio = 1.19 (95% CI: 0.91-1.55)]. Analysis of food records showed that fat intake at baseline and after randomization was not associated with total breast cancer incidence. Greater weight and lower carbohydrate intake at baseline and after randomization were associated with an increased risk of estrogen receptor (ER)-positive breast cancer. Our findings suggest that a sustained reduction in dietary fat intake did not reduce risk of breast cancer in women with extensive mammographic density. Weight and carbohydrate intakes were associated with risk of ER-positive 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".