Effectiveness of prehabilitation on outcomes following total knee and hip arthroplasty for osteoarthritis: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Purpose To quantify the effectiveness of prehabilitation prior to total knee and hip arthroplasty (TKA/THA) for osteoarthritis on postoperative outcomes assessed by self-report and performance-based measures.Methods Embase, MEDLINE, CENTRAL, CINAHL and Scopus (inception-August 2022) were searched for randomized controlled trials. Self-report outcomes were function, health-related quality of life (HRQoL), and pain. Performance-based outcomes were strength, range of motion (ROM), balance, and functional mobility. The RoB 2.0 assessed risk of bias. Random-effects meta-analysis was performed up to 52 weeks after TKA/THA.Results High risk of bias was found in 24 of 28 trials. Prehabilitation improved function (SMD = 0.50 [95%CI: 0.23, 0.77]), pain (SMD = 0.44 [95%CI: 0.17, 0.71]), HRQoL (SMD = 0.28 [95%CI: 0.12, 0.43]), strength (SMD = 0.72 [95%CI: 0.47, 0.98]), ROM (SMD = 0.31 [95%CI: 0.02, 0.59]), and functional mobility (SMD = 0.39 [95%CI: 0.05, 0.73]) post-TKA. No significant effect of prehabilitation on balance (SMD = 0.28 [95%CI: −0.11, 0.66]) post-TKA. All outcomes assessed had significant heterogeneity (p < 0.01). There were limited and contradictory trials (n = 2) for THA.Conclusion High risk of bias and significant heterogeneity observed in our meta-analysis prevent conclusions regarding prehabilitation effectiveness on outcomes up to one year after TKA/THA.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".