Virtual Reality–Assisted Rehabilitation for Patients Undergoing Total Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to assess the effectiveness of virtual reality-assisted rehabilitation in postoperative rehabilitation after total knee arthroplasty). DESIGN: This is a systematic review and meta-analysis of randomized controlled trials evaluating virtual reality-assisted rehabilitation in patients who have undergone total knee arthroplasty. The literature search included multiple databases, including PubMed, Embase, Web of Science, Cochrane, Scopus, PsycINFO, PEDro, CNKI, and Wanfang, with the final search date being May 20, 2024. RESULTS: Virtual reality-assisted rehabilitation for patients undergoing total knee arthroplasty showed lower Visual Analog Scale pain scores, better Western Ontario and McMaster Universities Osteoarthritis Index scores, improved Hospital for Special Surgery scores, shorter Timed Up and Go times, higher Berg Balance Scale scores, and greater knee range of motion than those of the control group. CONCLUSIONS: Virtual reality-assisted rehabilitation effectively reduced postsurgical pain and enhanced the recovery of function, mobility, balance, and range of motion in patients undergoing total knee arthroplasty. Integrating virtual reality-assisted rehabilitation into standard rehabilitation programs can optimize the outcomes of patients undergoing total knee arthroplasty.PROSPERO Registration: CRD42024596255.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".