Definitions of clinical study outcome measures for cardiovascular diseases: the European Unified Registries for Heart Care Evaluation and Randomized Trials (EuroHeart).
Bibliographic record
Abstract
BACKGROUND AND AIMS: Standardized definitions for outcome measures in randomized clinical trials and observational studies are essential for robust and valid evaluation of medical products, interventions, care, and outcomes. The European Unified Registries for Heart Care Evaluation and Randomised Trials (EuroHeart) project of the European Society of Cardiology aimed to create international data standards for cardiovascular clinical study outcome measures. METHODS: The EuroHeart methods for data standard development were used. From a Global Cardiovascular Outcomes Consortium of 82 experts, five Working Groups were formed to identify and define key outcome measures for: cardiovascular disease (generic outcomes), acute coronary syndrome and percutaneous coronary intervention (ACS/PCI), atrial fibrillation (AF), heart failure (HF) and transcatheter aortic valve implantation (TAVI). A systematic review of the literature informed a modified Delphi method to reach consensus on a final set of variables. For each variable, the Working Group provided a definition and categorized the variable as mandatory (Level 1) or optional (Level 2) based on its clinical importance and feasibility. RESULTS: Across the five domains, 24 Level 1 (generic: 5, ACS/PCI: 8, AF: 2; HF: 5, TAVI: 4) and 48 Level 2 (generic: 18, ACS-PCI: 7, AF: 6, HF: 2, TAVI: 15) outcome measures were defined. CONCLUSIONS: Internationally derived and endorsed definitions for outcome measures for a range of common cardiovascular diseases and interventions are presented. These may be used for data alignment to enable high-quality observational and randomized clinical research, audit, and quality improvement for patient benefit.
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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 | 0.413 | 0.472 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".