Adherence to a cholesterol-lowering diet and the risk of prostate cancer
Bibliographic record
Abstract
Evidence suggests a role of serum cholesterol in prostate cancer (PCa) development and of lipid lowering medications in PCa risk reduction. We developed a score for adherence to an established cholesterol-lowering diet and evaluated its association with PCa risk in a multicentric hospital-based case-control study (1294 cases; 1451 matched controls) in Italy (1992-2001). The score was derived from seven dietary indicators which have been reported to lower cholesterol levels: high intake of non-cellulosic polysaccharides (viscous fibres), monounsaturated fatty acids, legumes, seeds/corn oil; low intake of saturated fatty acids, dietary cholesterol, and glycaemic index. Odds ratios (ORs) and corresponding confidence intervals (CIs) were calculated through the unconditional logistic regression model. Although most of the dietary indicators alone were not significantly associated with reduced PCa risk, men who fulfilled 5 to 7 dietary indicators (187 cases and 281 controls) showed a 43% reduction in PCa risk compared to those with 0 to 2 indicators (OR: 0.57; 95% CI: 0.43-0.77). This association was not modified by socio-demographic characteristics or lifestyle factors. In conclusion, adherence to a cholesterol-lowering diet is a favourable factor against the risk of PCa, providing support to dietary guidelines that promote cholesterol reduction through plant-based diets.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".