189-LB: LDL Cholesterol Reduction and Cardiovascular Outcomes in High-Risk Primary Prevention Patients
Bibliographic record
Abstract
Background: Current guidelines recommend reducing LDL cholesterol in patients at high risk for a first major adverse cardiovascular event (primary prevention). However, these recommendations are predominately based on clinical trials conducted many years ago. Most contemporary trials of LDL-cholesterol lowering therapies have enrolled only secondary prevention patients. Some authorities have questioned whether the benefits of cholesterol lowering exceed the harms in primary prevention patients. Currently, lipid-lowering therapies are underutilized in high-risk patients without a prior event, particularly in women and patients with diabetes. More than half of eligible patients are not currently receiving LDL-cholesterol lowering therapies. Methods: The CLEAR Outcomes trial reported cardiovascular outcomes for bempedoic acid treatment compared with placebo in a mixed population of primary and secondary prevention patients unable or unwilling to take guideline-recommended doses of statins (N=13,970). The primary end point was a composite of death from cardiovascular causes, nonfatal myocardial infarction (MI), nonfatal stroke, or coronary revascularization. The results of the CLEAR OUTCOMES trial were presented and simultaneously published in the NEJM on March 4. The hazard ratio for the primary end point for the full population was 0.87 (95% CI 0.79-0.96), P=0.004. Results: In the CLEAR Outcomes trial, 4206 patients (30%) met high risk primary prevention entry criteria, two thirds with diabetes. The HR in these primary prevention patients was lower than the HR for secondary prevention patients, 0.68 (95% CI 0.53-0.87), with a significant interaction P value (0.03). The current Late Breaking Trial will report detailed results for the primary and all key secondary endpoints for the 4206 patients enrolled based on high risk primary prevention criteria. Full results will be available in mid-April for simultaneous presentation and publication during the ADA meeting. Disclosure S. Nissen: Research Support; Eli Lilly and Company, AbbVie Inc., AstraZeneca, Bristol-Myers Squibb Company, ESPERION Therapeutics, Inc., New Amsterdam Pharma, Novartis, Patient-Centered Outcomes Research Institute, Silence Therapeutics. For the clear outcomes trial investigators: n/a. Funding National Heart, Lung, and Blood Institute (K23HL153774 to J.E.T.); University of Toronto (to B.R.S.)
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".