Endpoint adjudication in cardiovascular clinical trials
Bibliographic record
Abstract
Endpoint adjudication (EA) is a common feature of contemporary randomized controlled trials (RCTs) in cardiovascular medicine. Endpoint adjudication refers to a process wherein a group of expert reviewers, known as the clinical endpoint committee (CEC), verify potential endpoints identified by site investigators. Events that are determined by the CEC to meet pre-specified trial definitions are then utilized for analysis. The rationale behind the use of EA is that it may lessen the potential misclassification of clinical events, thereby reducing statistical noise and bias. However, it has been questioned whether this is universally true, especially given that EA significantly increases the time, effort, and resources required to conduct a trial. Herein, we compare the summary estimates obtained using adjudicated vs. non-adjudicated site designated endpoints in major cardiovascular RCTs in which both were reported. Based on these data, we lay out a framework to determine which trials may warrant EA and where it may be redundant. The value of EA is likely greater when cardiovascular trials have nuanced primary endpoints, endpoint definitions that align poorly with practice, sub-optimal data completeness, greater operator variability, and lack of blinding. EA may not be needed if the primary endpoint is all-cause death or all-cause hospitalization. In contrast, EA is likely merited for more nuanced endpoints such as myocardial infarction, bleeding, worsening heart failure as an outpatient, unstable angina, or transient ischaemic attack. A risk-based approach to adjudication can potentially allow compromise between costs and accuracy. This would involve adjudication of a small proportion of events, with further adjudication done if inconsistencies are detected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.220 | 0.464 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".