Postoperative Rehabilitation Interventions in Patients at Risk of Poorer Outcomes Following Total Knee Arthroplasty: A Systematic Review
Bibliographic record
Abstract
PURPOSE: This systematic review evaluated the efficacy of postoperative rehabilitation for patients at risk of poorer outcomes after total knee arthroplasty. METHODS: Six databases were searched, and only randomised controlled trials were included. Two reviewers independently screened, extracted data, and appraised the quality of the studies. RESULTS: Twenty-three studies were included. Insufficient evidence supports cognitive behavioural therapy for patients with kinesiophobia, while continuous passive motion may not be effective for range of motion deficits. The impact of exercise therapy on functional recovery is conflicting; however, longer durations tend to be more effective for older patients and those with functional limitations. Limited evidence suggests that exercise may not alleviate pain. Outpatient exercise therapy improves health-related quality of life more than home-based programs for older patients and those with functional limitations. The efficacy of other rehabilitation aspects remains inconclusive due to high heterogeneity in interventions and outcome measures, and high or unclear risk of bias in most studies. CONCLUSION: While limited evidence suggests benefits for specific rehabilitation interventions, there is insufficient data to assess the efficacy of most interventions on postoperative recovery in those at risk of poorer outcomes. More robust evidence is needed to guide clinical practice and standardise outcome measures. TRAIL REGISTRATION: PROSPERO CRD42022355574.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".