Clinical Endpoints in Pragmatic Heart Failure Trials: From Data Collection to Clinical Endpoint Classification
Bibliographic record
Abstract
Clinical endpoint classification (CEC)-that is, evaluation of clinical events using pre-defined criteria-is commonly conducted in clinical trial operations to ensure systematic and consistent assessment of endpoints needed to assess the intervention's safety and efficacy. This is particularly relevant for heart failure (HF) trials given the subjective decision-making around hospitalizations and variation in how worsening HF events are managed (both in hospital and in ambulatory settings). Several CEC strategies have been adopted to address the growing need for pragmatic clinical trials that enhance generalizability and minimize research burden on trial sites and patients. This review summarizes common CEC strategies including the traditional approach, investigator-reported endpoints, CEC using real-world data and CEC utilizing large language models. We summarize CEC strategies used in recent HF pragmatic trials and present challenges and considerations for CEC in HF pragmatic trials from the selection of clinical endpoints and data collection to CEC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".