Variation in Outcome Measures in Hip and Knee Arthroplasty Clinical Trials: A Proposed Approach to Achieving Consensus
Bibliographic record
Abstract
OMERACT began work over a decade ago on a consensus effort to identify optimal outcome measures for knee and hip osteoarthritis clinical trials. Recent evidence indicates extensive variation in outcome measures used in clinical trials of knee and hip arthroplasty published since 2000. This heterogeneity leads to confusion, not only for conducting systematic reviews but also for applying evidence to clinical practice. Given the extensive psychometric research conducted in the past 2 decades, the timing seems ideal to design and implement a study to develop consensus on optimal outcome measures for hip and knee arthroplasty trials. We describe a Delphi survey design and an approach for synthesizing the extensive psychometric literature on the outcome measures used in hip and knee arthroplasty trials. Plans for dissemination of the findings are also discussed. This proposed study could have an important influence on the design and reporting of future randomized trials of knee arthroplasty.
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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 | 0.840 | 0.835 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.013 | 0.011 |
| 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; 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".