Use and Interpretation of Composite End Points in Orthopaedic Trials
Bibliographic record
Abstract
Randomized controlled trials in orthopaedics are often underpowered to detect important differences in outcomes. Composite end points (CEPs) hold promise as a strategy to address this issue by combining multiple end points into one summary measure, thus increasing the observed event rate. The use of CEPs by trialists, however, can be problematic when they include components that vary greatly in importance to patients and when differences in apparent effect between components are large. We present an overview of CEPs with a focus on appropriate design and interpretation of results.
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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 | 0.740 | 0.902 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".