Session 4: CVD, diabetes and cancer A dietary portfolio for management and prevention of heart disease
Bibliographic record
Abstract
CHD is the leading cause of worldwide mortality. The prevalence of heart disease has been linked to the adoption of a sedentary lifestyle and the increased dietary dependence on saturated fats from animal sources and the intake of refined foods. Elevated blood cholesterol level is one of the major risk factors for CHD. While cholesterol-lowering drug therapy (statins) has been effective in reducing the risk of heart disease, there are those individuals who are unwilling or because of muscle pains or raised levels of liver or muscle enzymes are unable to take cholesterol-lowering medication. Fortunately, there is evidence linking a number of dietary components to CHD risk reduction. The strength of this evidence has prompted various regulatory bodies to advocate diet as the first line of defence for primary prevention of heart disease. It was therefore decided to combine four dietary components that have been shown to lower blood cholesterol concentrations (nuts, plant sterols, viscous fibre and vegetable protein) in a dietary portfolio in order to determine whether the combined effect is additive. In a metabolically-controlled setting this dietary portfolio has proved to be as effective as a starting dose of a first-generation statin cholesterol-lowering medication in reducing the risk of CHD. The dietary portfolio has also been shown to be effective in sustaining a clinically-significant effect in the long term under a 'real-world' scenario. However, success of the diet depends on compliance and despite the accessibility of the foods adherence has been found to vary greatly. Overall, the evidence supports the beneficial role of the dietary portfolio in reducing blood cholesterol levels and CHD risk.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.156 | 0.054 |
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".