Bibliographic record
Abstract
BACKGROUND: Many patients at high risk for coronary heart disease (CHD) fail to reach target lipid levels with currently available medications, and a small but clinically relevant proportion of patients experience adverse effects. Thus, additional pharmaceutical strategies are required to fill these gaps in efficacy and tolerability. OBJECTIVE: To provide an overview of both current and emerging antidyslipidemic drugs. METHODS: For the current antidyslipidemic drugs, we focus primarily on statins, bile acid sequestrants, fibrates, ezetimibe, and niacin. Emerging antidyslipidemic drugs herein discussed were identified by searching the Pharmaprojects database for 'hypercholesterolemia drugs' (Phase II or Phase III), 'HDL-based therapies', and 'PCSK9 inhibition'. RESULTS/CONCLUSIONS: Combinations of currently existing medications are most easily applicable. Meanwhile, strategies to raise HDL-C rely on a deep understanding of the complexity of HDL metabolism. Furthermore, novel approaches to further reduce LDL-C warrant careful evaluation of benefit-risk ratio. Finally, the medical community will have to rely on late-phase CHD outcome studies as the final arbiter of clinical application for any new antidyslipidemia treatment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".