Cholesteryl Ester Transfer Protein Inhibitors and Cardiovascular Outcomes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Cholesteryl ester transfer protein (CETP) inhibitors increase serum high-density lipoprotein cholesterol (HDL-c) concentration; however, their impact on cardiovascular outcomes is not clear. This systematic review examines the effect of CETP inhibitors on serum lipid profiles, cardiovascular events, and all-cause mortality. METHODS: We searched MEDLINE, Embase, and the Cochrane Library of Clinical Trials for placebo-controlled randomized controlled trials (RCTs) that examined the effect of a CETP inhibitor (dalcetrapib, anacetrapib, evacetrapib, or TA-8995) on all-cause mortality, major adverse cardiovascular events (MACE), or the components of MACE at ≥6 months. Data were pooled using random-effects models. RESULTS: A total of 11 RCTs (n = 62,431) were included in our systematic review; 4 examined dalcetrapib (n = 16,612), 6 anacetrapib (n = 33,682), and 1 evacetrapib (n = 12,092). Compared to dalcetrapib, ana-cetrapib and evacetrapib were more efficacious at raising HDL-c levels (∼100-130 vs. ∼30%). Anacetrapib and evacetrapib also decreased low-density lipoprotein cholesterol (LDL-c) by approximately 30% while dalcetrapib did not affect the LDL-c level. Overall, CETP inhibitors were not associated with the incidence of MACE (pooled relative risk [RR]: 0.97; 95% confidence interval [CI]: 0.91-1.04). CETP inhibitors may decrease the risks of nonfatal myocardial infarction (MI) (RR: 0.93; 95% CI: 0.87-1.00) and cardiovascular death (RR: 0.92; 95% CI: 0.83-1.01), though these trends did not reach statistical significance. CONCLUSIONS: CETP inhibitors are not associated with an increased risk of MACE or all-cause mortality. There is a trend towards small reductions in nonfatal MI and cardiovascular death, though the clinical im-portance of such reductions is likely modest.
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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 | 0.038 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.258 | 0.110 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it