Characterization of cardiovascular clinical events and impact of event adjudication on the treatment effect of darapladib versus placebo in patients with stable coronary heart disease: Insights from the STABILITY trial
Bibliographic record
Abstract
BACKGROUND: Clinical Endpoint Classification (CEC) in clinical trials allows FOR standardized, systematic, blinded, and unbiased adjudication of investigator-reported events. We quantified the agreement rates in the STABILITY trial on 15,828 patients with stable coronary heart disease. METHODS: Investigators were instructed to report all potential events. Each reported event was reviewed independently by 2 reviewers according to prespecified processes and prespecified end point definitions. Concordance between reported and adjudicated cardiovascular (CV) events was evaluated, as well as event classification influence on final study results. RESULTS: In total, CEC reviewed 7,096 events: 1,064 deaths (696 CV deaths), 958 myocardial infarctions (MI), 433 strokes, 182 transient ischemic attacks, 2,052 coronary revascularizations, 1,407 hospitalizations for unstable angina, and 967 hospitalizations for heart failure. In total, 71.8% events were confirmed by CEC. Concordance was high (>80%) for cause of death and nonfatal MI and lower for hospitalization for unstable angina (25%) and heart failure (50%). For the primary outcome (composite of CV death, MI, and stroke), investigators reported 2,086 events with 82.5% confirmed by CEC. The STABILITY trial treatment effect of darapladib versus placebo on the primary outcome was consistent using investigator-reported events (hazard ratio 0.96 [95% CI 0.87-1.06]) or adjudicated events (hazard ratio 0.94 [95% CI 0.85-1.03]). CONCLUSIONS: The primary outcome results of the STABILITY trial were consistent whether using investigator-reported or CEC-adjudicated events. The proportion of investigator-reported events confirmed by CEC varied by type of event. These results should help improve event identification in clinical trials to optimize ascertainment and adjudication.
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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 | 0.178 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".