Bibliographic record
Abstract
PURPOSE OF REVIEW: We provide an overview of orally administered lipid-lowering therapies under development. RECENT FINDINGS: Recent data support statins for intermediate risk primary prevention, and ezetimibe for high-risk secondary prevention. Novel agents in development include bempedoic acid and gemcabene, and work continues on one remaining cholesteryl ester transfer protein inhibitor, anacetrapib, to determine whether this class can reduce cardiovascular risk. Selective peroxisome proliferator-activated receptor modulators such as K-877 are under study to determine whether they have an advantage over older fibrates. Diacylglycerol transferase inhibitors such as pradigastat appear to have potent triglyceride-lowering effects, even for patients with familial chylomicronemia syndrome. Finally, novel ω-3 preparations are available with significant triglyceride lowering, although their role in therapy remains unclear. SUMMARY: Statins will remain the backbone of lipid-lowering therapy, although several novel oral agents are promising. The common theme across drugs in development is the demonstration of good lipid-lowering effect, although lacking cardiovascular outcomes data, which will likely be necessary before any of them, can be recommended or approved for widespread use.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.009 | 0.005 |
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".