Role of Lipoprotein Apheresis in Cardiovascular Disease Risk Reduction
Bibliographic record
Abstract
BACKGROUND AND AIM: Elevated low-density lipoprotein cholesterol and/or lipoprotein(a) are established risk factors for cardiovascular disease (CVD). Management of hypercholesterolemia consists of drug therapies, including statins and proprotein convertase subtilisin/kexin type 9 inhibitors. In patients with familial hypercholesterolemia (FH), lipoprotein apheresis (LA) is utilized to control lipid levels. However, LA is not currently a standard therapy for non-FH. This review summarizes the literature regarding LA therapy in CVD prevention. METHODS: PubMed/MEDLINE databases were searched using the keywords "LA" and "CVD". Citations were individually reviewed for relevance. RESULTS: The efficacy of LA was clearly demonstrated, largely based on evidence from observational studies. In patients who are unresponsive to traditional lipid-lowering medications, LA effectively reduced serum lipoprotein levels and adverse cardiovascular events. CONCLUSION: It was concluded that LA is a safe and effective technique that could be considered in the management of hypercholesterolemia and future risk. Randomized control trials would further support a role for LA as a therapeutic option.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".