Systematic review of measurement properties of patient-reported outcome measures used in patients undergoing hip and knee arthroplasty
Bibliographic record
Abstract
OBJECTIVES: To identify patient-reported outcome measures (PROMs) that have been developed and/or used with patients undergoing hip or knee replacement surgery and to provide a shortlist of the most promising generic and condition-specific instruments. METHODS: A systematic review of the literature was performed to identify measures used in patients undergoing hip and knee replacement and extract and evaluate information on their methodological quality. RESULTS: Thirty-two shortlisted measures were reviewed for the quality of their measurement properties. On the basis of the review criteria, the measures with most complete evidence to date are the Oxford Hip Score (OHS) (for patients undergoing hip replacement surgery) and the Oxford Knee Score (OKS), with OKS-Activity and Participation Questionnaire (for patients undergoing knee replacement surgery). CONCLUSION: A large number of these instruments lack essential evidence of their measurement properties (eg, validity, reliability, and responsiveness) in specific populations of patients. Further research is required on almost all of the identified measures. The best-performing condition-specific PROMs were the OKS, OHS, and Western Ontario and McMaster Universities Osteoarthritis Index. The best-performing generic measure was the Short Form 12. Researchers can use the information presented in this review to inform further psychometric studies of the reviewed measures.
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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 | 0.015 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".