Inhibitors in the Management of Patients with Atherosclerotic Cardiovascular Diseases: Guidelines and Reimbursement Issues
Bibliographic record
Abstract
Current guidelines for the management of patients with dyslipidemia define low-density lipoprotein cholesterol (LDL-C) as the primary target in addressing lipid-lowering therapy. The target level of LDL-C in real clinical practice is achieved in no more than a third of patients who have undergone a coronary event and receive high-intensity lipid-lowering therapy. Achieving the goals of lipid-lowering therapy in a significant proportion of patients with atherosclerotic cardiovascular diseases (ACVD) is impossible with the use of even high doses of statins, which requires its enhancement by other drugs. The article considers the place of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors in the prevention of cardiovascular diseases in patients with ACVD in accordance with the latest Russian and international guidelines. A modern decision-making algorithm for the initiation of PCSK9 inhibitors therapy in patients with ACVD is presented. The authors provide a clear understanding about the patient populations that will benefit most from the taking of PCSK9 inhibitors. Particular attention is paid to Guidelines for the management of dyslipidemias developed by European Society of Cardiology and European Atherosclerosis Society in 2019. The issues of patients provision with PCSK9 inhibitors with reference to Russian conditions are described in details in accordance with the requirements for territorial programs of state guarantees. Further improvement in the provision of PCSK9 inhibitors to patients with indications for this therapy is necessary, considering the potential of these drugs in reducing cardiovascular morbidity and mortality in patients with ACVD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".