Adherence to a Cholesterol‐Lowering Diet and the Risk of Female Hormone‐Related Cancers: An Analysis From a Case–Control Study Network
Bibliographic record
Abstract
OBJECTIVE: We investigated the association between a cholesterol-lowering diet score and the risk of female hormone-related cancers. DESIGN: We used data on 2108 breast, 367 endometrial, 869 ovarian cancer cases and corresponding controls from an Italian network of case-control studies. SETTING: Hospital-based. SAMPLE: Breast, endometrial, and ovarian cancer cases and controls. METHODS: We assessed the adherence to a cholesterol-lowering diet using a score based on seven dietary components: high intake of non-cellulosic polysaccharides, monounsaturated fatty acids, legumes, seeds/corn oil; low intake of saturated fatty acids, dietary cholesterol, and glycaemic index. We assigned one point for each component if the requirement was met; otherwise, we assigned zero. The overall score was calculated by summing up points over the seven components, ranging from 0 (null) to 7 (complete adherence). MAIN OUTCOME MEASURES: Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated through unconditional logistic regression models including terms for potential confounders. RESULTS: Compared to a low adherence to a cholesterol-lowering diet (0-2 points), the ORs for a higher adherence (5-7 points) were 0.74 (95% CI: 0.60-0.92) for breast, 0.48 (95% CI: 0.30-0.77) for endometrial, and 0.77 (95% CI: 0.57-0.99) for ovarian cancer. The ORs for a 1-point increment in the score were 0.87 (95% CI: 0.97-0.80), 0.80 (95% CI: 0.72-0.90), and 0.90 (95% CI: 0.84-0.97) for breast, endometrial, and ovarian cancers, respectively. CONCLUSIONS: A cholesterol-lowering diet may favourably affect the risk of female hormone-related cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".