Virtual reality rehabilitation following total knee arthroplasty: a systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
PURPOSE: The use of virtual reality (VR) based rehabilitation has increased substantially within orthopedic surgery, particularly in the field of total knee arthroplasty (TKA). The objective of this systematic review and meta-analysis was to compare patient-reported outcomes and cost analyses from randomized controlled trials (RCT) utilizing VR-based rehabilitation in patients following TKA. METHODS: MEDLINE, EMBASE, and Cochrane databases were searched for RCTs involving VR-based rehabilitation following TKA. Quantitative synthesis was conducted for pain scores and functional outcomes. Narrative outcomes were reported for results not amenable to quantitative synthesis. RESULTS: A total of 9 RCTs with 835 patients were included with follow-up ranging from 10 days to 6 months postoperatively. No differences in pain scores were demonstrated between VR-based and traditional rehabilitation at 2 weeks and 3 months postoperatively. VR-based rehabilitation demonstrated improved functional outcomes at 12 weeks (n = 353) postoperatively [mean difference (MD) - 3.32, 95% confidence interval (CI) - 5.20 to - 1.45, moderate certainty evidence] and 6 months (n = 66) postoperatively [MD - 4.75, 95% CI - 6.69 to - 2.81, low certainty evidence], compared to traditional rehabilitation. One trial demonstrated significant cost savings with the use of VR-based rehabilitation. CONCLUSIONS: VR-based rehabilitation for patients undergoing TKA represents an evolving field that may have advantages over traditional therapy for some patients. The current review is limited by the low quality of evidence in the literature. This is a rapidly evolving field with more trials needed to determine the impact of VR-based rehabilitation on patients undergoing TKA. LEVEL OF EVIDENCE: Level I; meta-analysis of randomized controlled trials.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".