Long Term Effectiveness of A Dietary Portfolio of Cholesterol‐Lowering Foods in Hypercholesterolemic Subjects
Bibliographic record
Abstract
Objective: To determine the long term effectiveness of advice to consume a dietary portfolio of cholesterol‐lowering foods on risk factors for heart disease. Methods: Sixty‐six hyperlipidemic subjects were prescribed diets high in plant sterols (1.0 g/1000 kcal), soy protein (22.5 g/1000 kcal), viscous fibers (10 g/1000 kcal) and almonds (23 g/1000 kcal). Thirty subjects completed 3 years. Results: LDL‐cholesterol (LDL‐C) was significantly reduced at years 1, 2, and 3 with mean reductions of 17.7%, 11.2%, and 10.1%, respectively (P<0.01, n=30). HDL‐cholesterol (HDL‐C) was significantly raised at years 1, 2, and 3 with mean increases of 3.9%, 3.1% and 4.5%, respectively (P<0.01, n=30). Changes in LDL‐C significantly correlated with compliance to the dietary portfolio. Subjects with greater than 75% compliance to the dietary portfolio achieved a mean 24% reduction in LDL‐C. Conclusions: A sustained long term benefit of the dietary portfolio was seen in hyperlipidemic subjects with lower LDL‐C and raised HDL‐C over a 3‐year period. The effectiveness of the dietary portfolio was greater with >20% LDL‐C reductions seen in subjects having over 75% compliance. Funding: Canada Research Chair Endowment of the Federal Government of Canada; the Canadian Natural Sciences and Engineering Research Council of Canada; Loblaw Brands Limited; the Almond Board of California; Unilever Canada and Unilever Research & Development, Vlaardingen, The Netherlands; and Yves Veggie Cuisine.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".