New therapies to reduce low-density lipoprotein cholesterol
Bibliographic record
Abstract
PURPOSE OF REVIEW: Lipid-lowering is an intervention that reduces atherosclerosis and its complications. Statins currently form the standard of care but are not able to reduce low-density lipoprotein cholesterol (LDL-C) adequately in all patients - particularly those with familial hypercholesterolaemia and those with statin intolerance. RECENT FINDINGS: Combination therapy with statins is well established and ezetimibe is often used as an additional LDL-C-lowering agent reducing LDL-C by 20%. However, its clinical efficacy still remains controversial. Newer, more potent methods of LDL-C reduction are in development. Both lomitapide, a microsomal transfer protein inhibitor (MTPI), and mipomersen, an antisense oligonucleotide (ASO), have been shown to improve LDL-C levels by 25-50% in patients with homozygous familial hypercholesterolaemia. In patients with heterozygous familial hypercholesterolaemia or statin intolerance antibody-based inhibitors of preprotein convertase subtilisin/kexin 9 (PCSK9) produce reductions in LDL-C of 30-65%. Cholesterol ester transfer protein inhibitors (CETPIs) reduce LDL-C by 30-40% as well as raising levels of high-density lipoprotein cholesterol (HDL-C) and may also have a role as additional LDL-C-reducing drugs. SUMMARY: Surrogate outcome trials will be required with lomitapide or mipomersen to confirm their effects in homozygous familial hypercholesterolaemia and clinical endpoint trials will be needed for PCSK9 and CETPIs if these are to be used widely.
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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 | 0.001 | 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) | 0.014 | 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".