Effect of bempedoic acid on mortality and cardiovascular events in primary and secondary prevention: A post-hoc analysis of the CLEAR-outcomes trial
Bibliographic record
Abstract
BACKGROUND: The effects of bempedoic acid on mortality in the secondary prevention setting have not been examined. METHODS: We used data from the overall and primary prevention reports of CLEAR - Outcomes to reconstruct data for the secondary prevention population. A Bayesian analyses was employed to calculate the posterior probability of benefit or harm for the outcomes of all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). Relative effect sizes are presented as risk ratios (RR) with 95% credible intervals (CrI), which represent the intervals that true effect sizes are expected to fall in with 95% probability, given the priors and model. RESULTS: In primary prevention, the posterior probability of bempedoic acid decreasing all-cause and cardiovascular mortality was 99.4% (RR: 0.70; 95% CrI: 0.51 to 0.92) and 99.7% (RR: 0.58; 95% CrI: 0.38 to 0.86) respectively. In secondary prevention, the posterior probability of bempedoic acid increasing all-cause and cardiovascular mortality was 96.6% (RR: 1.15; 95% CrI: 0.99 to 1.33) and 97.2% (RR: 1.21; 95% CrI: 1.00 to 1.45) respectively. The probability of bemepdoic acid reducing MACE in the primary and secondary prevention settings was 99.9% (RR: 0.70; 95% CrI: 0.54 to 0.88) and 95.8% (RR: 0.92; 95% CrI: 0.84 to 1.01) respectively. CONCLUSION: In contrast to its effect in the primary prevention subgroup of CLEAR - Outcomes, bempedoic acid resulted in a more modest MACE reduction and a potential increase in mortality in the secondary prevention subgroup. Whether these findings represent true treatment effect heterogeneity or the play of chance requires further evidence.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".