Implementation of standardized assessment and reporting of myocardial infarction in contemporary randomized controlled trials: a systematic review
Bibliographic record
Abstract
Myocardial infarction (MI) is a key endpoint in randomized controlled trials (RCTs), but heterogeneous definitions limit comparisons across RCTs or meta-analyses. The 2000 European Society of Cardiology/American College of Cardiology MI redefinition and the 2007 universal MI definition consensus documents made recommendations to address this issue. In cardiovascular randomized trials, we evaluated the impact of implementation of three key recommendations from these reports-troponin use to define MI; separate reporting of spontaneous and procedure-related MI; and infarct size reporting. We searched ClinicalTrials.gov and MEDLINE databases for cardiovascular RCTs with more than 500 patients in which enrolment began between September 2000 and July 2012 and that listed MI in the primary endpoint. We searched English-language publications with primary results or design papers. Of 3222 studies screened, 96 (3.0%) met our criteria. We extracted enrolment start date, number of patients and MI events, follow-up duration, and coronary revascularization rate. Data extraction quality was assessed by duplicated extractions. Of 96 RCTs, 80 had a primary results publication, comprising 608 091 patients and 43 621 endpoint MIs. Myocardial infarction represented 45.3% (95% confidence interval, 40.2-50.4) of events in the primary composite endpoint. Troponin defined MI in 57% (53/93) of trials with an MI definition available. Of these RCTs, three used troponin only if creatine kinase-MB was unavailable, six used troponin to define peri-procedural MI, seven specified the 99th percentile as the MI decision limit, and three reported spontaneous and procedure-related MI separately. None reported biomarker-based infarct size, but five reported MI as multiples of the assay upper limit of normal. Although MI is a major component of cardiovascular RCT primary endpoints, standardized MI reporting and implementation of consensus document recommendations for MI definition are limited. Developing appropriate strategies for uniform implementation is required.
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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 | 0.545 | 0.772 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.027 | 0.023 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".