Expanding the use and interpretation of patient-centric cardiovascular clinical trial endpoints
Bibliographic record
Abstract
Significant improvements have been achieved to enhance the patient-centricity of clinical research, including the development and utilization of novel clinical trial endpoints. These include endpoints that harness outcomes that are important to patients and reflect the patients' lived experiences. This may take the form of utilizing variables such as days alive and out of hospital (DAOH) and quality-of-life adjusted outcomes. The use of composite outcomes can be used to enrich patient-centricity by weighting or ranking events. These approaches have several nuances that should be considered including selecting appropriate events, defining outcomes, how to elicit or construct weights, and whose opinions to consider. After weights have been determined, a variety of approaches exist to combine weights with outcomes and make comparisons between groups. The approaches, including the win ratio, weighted win ratio, desirability of outcome ranking (DOOR), multicriteria decision analysis (MCDA), and variations of time-to-first composite event analyses, have unique advantages and challenges depending on the clinical scenario. While improving patient-centric outcomes is of high importance to multiple stakeholders, more comparative work is needed to characterize the implications of alternative approaches.
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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 | 0.132 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".