Eurasian Association of Cardiology (EAC)/ Russian National Atherosclerosis Society (RNAS) Guidelines for the diagnosis and correction of dyslipidemia for the prevention and treatment of atherosclerosis (2025)
Bibliographic record
Abstract
Cardiovascular diseases are one of the main causes of mortality and disability among the population of the member countries of the Eurasian Association of Cardiology, which is directly related to the high prevalence of risk factors for atherosclerosis, among which dyslipidemia plays a leading role. The previous version of the Eurasian Recommendations for the Diagnosis and correction of lipid metabolism disorders for the prevention and treatment of atherosclerosis was presented in 2020. Over the past 5 years, approaches to risk stratification, diagnosis of subclinical atherosclerosis, and correction of dyslipidemia using various classes of lipid-lowering drugs have changed significantly. Among the initial updates, proposals should be highlighted for determining the blood lipid level in each adult or upon reaching the age of 18, using the SCORE and/or SCORE2 scales to stratify cardiovascular risk, and non-invasive imaging techniques to assess subclinical atherosclerosis. In the sections devoted to therapy, fixed combinations of lipid-lowering drugs, inclisiran, and bempedoic acid are presented. The section on the prevalence, significance and approaches to correction of hypertriglyceridemia, as well as the section on extracorporeal hemocorrection, has been expanded. A chapter on lipoprotein(a) is introduced. Sections on correction of dyslipidemia in cerebrovascular diseases, heart transplantation and HIV infection are presented. These recommendations will be useful to the phisicians of all specialties for the effective management of their patients.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".