Red yeast rice for dyslipidaemias and cardiovascular risk reduction: A position paper of the International Lipid Expert Panel
Bibliographic record
Abstract
The risk of atherosclerotic cardiovascular disease (ASCVD) is strongly related to lifetime exposure to low-density lipoprotein (LDL)-cholesterol in longitudinal studies. Lipid-lowering therapy (using statins, ezetimibe and PCSK9 inhibitors) substantially ameliorates the risk and is associated with long-term reduction in cardiovascular (CV) events. The robust evidence supporting these therapies supports their continued (and expanding) role in risk reduction. In addition to these 'conventional' therapeutics, while waiting for other innovative therapies, growing evidence supports the use of a range of 'nutraceuticals' (constituents of food prepared as pharmaceutical formulations) including preparations of red yeast rice (RYR), the product of yeast (Monascus purpureus) grown on rice, which is a constituent of food and is used in traditional Chinese medicine. The major active ingredient, monacolin K, is chemically identical to lovastatin. RYR preparations have been demonstrated to be safe and effective in reducing LDL-C, and CV events. However, surprisingly, RYR has received relatively little attention in international guidelines - and conventional drugs with the strongest evidence for event reduction should always be preferred in clinical practice. Nevertheless, the absence of recommendations relating to RYR may preclude the use of a product which may have clinical utility in particular groups of patients (who may anyway self-prescribe this product), what in the consequence might help to reduce population CV risk. This Position Paper of the International Lipid Expert Panel (ILEP) will use the best available evidence to give advice on the use of red-yeast rice in clinical practice.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".