Preferred Outcome Measures Used in Randomized Clinical Trials of Total Knee Replacement Rehabilitation: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To determine the most frequently used outcome measures in total knee replacement rehabilitation trials. LITERATURE SURVEY: Systematic review of randomized trials searched in five databases: Web of Science, MEDical Literature Analysis and Retrieval System, Physiotherapy Evidence Database, Scopus, and Cochrane Library. METHODOLOGY: Trials were included if participants underwent total knee replacement rehabilitation and outcome measures were used to assess rehabilitation outcomes. A descriptive synthesis determined the frequency of using outcome measures and preferred assessment time points. Outcomes were classified into eight categories: patient- and clinician-reported function, performance-based function, balance, anxiety and depressive symptoms, quality of life, and others. SYNTHESIS: Eighty-one trials were included and 102 different outcome measures were classified. The most frequently reported outcome was knee range of motion, used in 54% of trials, followed by a visual analog scale of pain (43%) and Western Ontario and McMaster Universities Arthritis Index (WOMAC; 40%). Patient- and clinician-reported function were the categories most frequently assessed (74%), whereas performance-based measures were implemented by 56% of trials. The most frequent assessment time points were 1 week presurgery (52%) and 3 months postsurgery (39%). CONCLUSIONS: There is consensus regarding the need to evaluate functional outcomes in total knee replacement rehabilitation trials but none regarding the outcome measure that should be used. These findings suggest that most trials include patient- and clinician-reported functional measures, along with pain and performance-based measures in trial designs.
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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 | 0.172 | 0.453 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.029 | 0.021 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".