Clinical Outcome Endpoints in Heart Failure Trials: A European Society of Cardiology Heart Failure Association Consensus Document
Bibliographic record
Abstract
Endpoint selection is a critically important step in clinical trial design. It poses major challenges for investigators, regulators, and study sponsors, and it also has important clinical and practical implications for physicians and patients. Clinical outcomes of interest in heart failure trials include all-cause mortality, cause-specific mortality, relevant non-fatal morbidity (e.g., all-cause and cause-specific hospitalization), composites capturing both morbidity and mortality, safety, symptoms, functional capacity, and patient-reported outcomes. Each of these endpoints has strengths and weaknesses that create controversies regarding which is most appropriate in terms of clinical importance, sensitivity, reliability, and consistency. Not surprisingly, a lack of consensus exists within the scientific community regarding the optimal endpoint(s) for both acute and chronic heart failure trials. In an effort to address these issues, the Heart Failure Association of the European Society of Cardiology (HFA-ESC) convened a group of expert heart failure clinical investigators, biostatisticians, regulators, and pharmaceutical industry scientists (Nice, France, 12-13 February 2012) to evaluate the challenges of defining heart failure endpoints in clinical trials and to develop a consensus framework. This report summarizes the group's recommendations for achieving common views on heart failure endpoints in clinical trials.
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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 | 0.494 | 0.351 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.021 | 0.012 |
| Research integrity | 0.037 | 0.052 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".