A Plant-Based Cholesterol-Lowering Diet Score Correlates with Serum LDL-Cholesterol Levels
Bibliographic record
Abstract
BACKGROUND: A cholesterol-lowering diet score was previously developed for epidemiological studies; its association with serum lipid profile was not confirmed yet. METHODS: The score was developed as an adaptation of the dietary portfolio for cholesterol reduction, assigning one point for adherence to seven dietary indicators and ranging from 0 (null adherence) to 7 (highest adherence). The score was calculated for breast cancer patients enrolled in the DEDiCa study using a 7-day food record; serum lipid profile, including total and low-density lipoprotein cholesterol (LDL-C), was evaluated in serum at baseline. RESULTS: < 0.01). The proportion of women with LDL-C above the recommended limit of 116 mg/dL was 60.0% with score 0-1 and 42.6% with score ≥4. Although the score directly correlates with consumption of foods from vegetal sources, it was mildly associated with the healthful plant-based diet index (r-Spearman = 0.51) and the Mediterranean Diet Adherence Screener (r-Spearman = 0.30) Conclusions: These results provide experimental evidence that the cholesterol-lowering diet score is capable of detecting a specific plant-based dietary pattern that affects circulating cholesterol levels.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".