Bibliographic record
Abstract
Lipoprotein(a) (Lp(a)) is a type of lipoprotein consisting of low-density lipoprotein with apoprotein(a) (apo(a)) and is a risk factor for cardiovascular disease (CVD). Lowering Lp(a) levels may improve CVD outcomes, but this has been challenging owing to the unique structure and metabolic pathway of Lp(a). Recently, several new treatments using apo(a)-targeting drugs have been developed to reduce Lp(a) levels. Here, we briefly summarize the treatments, including earlier attempts at reducing Lp(a). Some lipid-lowering drugs can reduce Lp(a) levels in a non-targeted manner; while the effect of statins varies, niacin and proprotein convertase subtilisin/kexin type 9 inhibitors exhibit a reduction of over 20% in Lp(a) levels. Estrogen-related drugs and certain supplements can reduce Lp(a) levels, which may promote a deeper understanding of the modulation of Lp(a) levels. An apo(a) antisense oligonucleotide, small interfering RNAs, and a small molecule Lp(a)-formation inhibitor have recently been developed as promising drugs that specifically reduce Lp(a) levels by approximately 80%. The treatment strategies for Lp(a) are set to be updated, although we are awaiting clinical evidence on the reduction of CVD events by new treatments and the effective threshold for Lp(a) levels for the prevention of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".