The PCSK9 revolution and the potential of PCSK9-based therapies to reduce LDL-cholesterol
Bibliographic record
Abstract
[first paragraph of article]A large number of clinical trials over the last 30 years have firmly consolidated the importance of lowering low density lipoprotein cholesterol (LDLc) in the prevention of cardiovascular diseases (CVD) and its associated devastating sequelae. While healthy diets and exercise are highly recommended to lower LDLc levels, in many individuals with high baseline levels of LDLc, this is not sufficient to bring levels down to recommended target values in order to prevent recurrent coronary heart disease and cardiovascular complications. This is especially true for patients at high risk of premature cardiovascular death and disability, including those with familial hypercholesterolaemia (FH). FH is a very common inherited disease – affecting at least 30 million people worldwide, with an overall incidence of 1:200 globally – of whom ≤ 1% have been diagnosed. The advent of HMG-CoA reductase inhibitors, also known as ‘‘statins’’, and their first application to hypercholesterolemic patients over 30 years ago, has revolutionized the treatment of FH patients and resulted in substantial lowering of LDLc. In addition, cholesterol– lowering drugs, such as ‘‘ezetimibe’’ that blocks cholesterol absorption from the gut by inhibiting the Niemann-Pick C1-like 1 (NPC1L1) transporter, have also been successful and a 7-year IMPROVE-IT trial revealed that a ‘‘simvastatin-ezetimibe’’ combination resulted in an incremental lowering of LDLc levels and a modest 2% improved cardiovascular outcomes.3 Therefore, it became clear that additional treatments are needed to substantially decrease LDLc and efficiently protect against CVD.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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".