Bibliographic record
Abstract
The role of lipid lowering in reducing the risk of mortality and morbidity from cardiovascular disease (CVD) is well established. Treatment particularly aimed at decreasing low-density lipoprotein cholesterol (LDL-C) is effective in reducing the risk of death from coronary heart disease and stroke. Statins form the cornerstone of treatment. However, in some individuals with a high risk of CVD who are unable to achieve their target LDL-C due to either intolerance or lack of efficacy, there is the need for alternative therapies. This review provides an overview of the different classes of currently available lipid-lowering medications including statins, fibrates, bile acid sequestrants (resins), and omega-3 fatty acids. Data are presented on their indications, pharmacology, and the relevant end point clinical trial data with these drugs. It also discusses the human trial data on some novel therapeutic agents that are being developed including those for homozygous familial hypercholesterolemia--the antisense oligonucleotide mipomersen and the microsomal transfer protein inhibitor lomitapide. Data are presented on phase II and III trials on agents with potentially wider applications, cholesterol ester transfer protein inhibitors and proprotein convertase subtilisin kexin 9 inhibitors. The data on a licensed gene therapy for lipoprotein lipase deficiency are also presented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.020 |
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".