The comparison of telerehabilitation and face-to-face rehabilitation after total knee arthroplasty: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction The objective of this study was to assess the efficacy of telerehabilitation for patients after total knee arthroplasty (TKA) compared with face-to-face rehabilitation. Methods Medline, SCOPUS, Google Scholar, EMBASE, Springer, Science Direct, and Cochrane databases were searched electronically. Relevant journals and references of studies included were hand-searched for randomized controlled trials (RCTs) regarding the efficacy of telerehabilitation on functional recovery in patients after TKA. Two reviewers independently performed data extraction and quality assessment. Data were analysed using RevMan 5.3 software and Stata 12.0 software. Results Four RCTs involving 442 patients were included in the meta-analysis. Overall, compared with face-to-face rehabilitation, telerehabilitation could achieve comparable pain relief (mean difference = 0.52; 95% confidence interval (CI) = -0.20 to 1.24; p = 0.16) and better Western Ontario and McMaster Universities Osteoarthritis Index improvement (mean difference = 1.13; 95% CI = 0.23 to 2.02; p = 0.014). In addition, telerehabilitation treatment resulted in a significantly higher extension range ( p < 0.00001) and quadriceps strength ( p = 0.0002) than face-to-face rehabilitation. Discussion Telerehabilitation should be recommended for patients after TKA because of its comparable pain control and better improvement of functional recovery as compared to face-to-face rehabilitation.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".