Development and Validation of a Dietary Portfolio Score for use Among Hypercholesterolemic Individuals
Bibliographic record
Abstract
Background The Dietary Portfolio (DP) significantly improves serum low‐density lipoprotein (LDL) profiles in hypercholesterolemic individuals. We aimed to develop and validate a diet score based on the DP. Methods Hypercholesterolemic individuals who participated in a 6‐month DP trial were included. Four dietary components (soy protein, viscous fiber, plant sterols, and nuts) were identified a priori and each scored between 0‐10 units; components were summated to create a DP‐score (DPS) ranging from 0‐40 units. Multivariate linear regression quantified DPS with continuous variables; logistic regression compared individuals with 蠅‐25% ΔLDL vs. those without. Results Median end‐study DPS was 14 (range: 0‐40, n=238). A 1‐unit increase in DPS or ΔDPS decreased ΔLDL by ‐0.036mmol/L (‐0.81%, p<0.001), Δdiastolic blood pressure by ‐0.093mmHg (p=0.017), and Δ10‐year CHD risk by ‐0.048 (p<0.001). The odds of achieving a ‐25% ΔLDL was 23.8 fold (95%CI: 7.40 to 76.7) greater when comparing the highest and lowest adherence groups (31‐40 units vs. 0‐10 units). Conclusions DPS and ΔDPS predict beneficial cardiometabolic risk profiles. Future research should relate the DPS to the incidence of stroke and CHD. Funding CIHR, Loblaw Brands ltd, Unilever, Solae, AFM Net
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".