Abstract 15091: Significant Reductions in Both Adjudicated and Investigator-Reported Ischemic Events in REDUCE-IT
Bibliographic record
Abstract
Introduction: REDUCE-IT was an event-driven trial that randomized 8,179 statin-treated patients with controlled LDL-C and moderately elevated triglycerides to icosapent ethyl (IPE) 4g daily or placebo, with a median of 4.9 years of follow-up. There was a significant reduction in the prespecified adjudicated rates of the primary endpoint (cardiovascular [CV] death, non-fatal myocardial infarction [MI], non-fatal stroke, coronary revascularization, and unstable angina requiring hospitalization) and of the key secondary endpoint (CV death, MI, stroke), as well as in all the primary endpoint components. We sought to determine the effect of IPE on investigator-reported events. Methods: The Clinical Endpoint Committee (CEC) blindly adjudicated investigator-reported events according to a prespecified charter. Medical records and reports were also reviewed to assess for clinical events not reported by investigators. An endpoint management team compiled and electronically provided event packets to the CEC via an adjudication database. The CEC Chair provided final adjudication if the two primary adjudicators could not reach consensus. Results: IPE significantly reduced the rate of the primary endpoint (hazard ratio 0.74, p=0.0000000002) and the key secondary endpoint (hazard ratio 0.75, p=0.000007) as reported by the site investigators, with consistent benefits in each component of the primary endpoint (Table). There was a high degree of concordance between investigator-reported and adjudicated endpoints. Conclusions: Icosapent ethyl significantly reduced multiple types of ischemic events, both by independent, blinded adjudication as well as by investigator-reported assessment. These results underscore the robustness of the benefits of icosapent ethyl seen in REDUCE-IT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".