Effects of Preoperative Telerehabilitation on Muscle Strength, Range of Motion, and Functional Outcomes in Candidates for Total Knee Arthroplasty: A Single-Blind Randomized Controlled Trial
Bibliographic record
Abstract
This study aims to investigate the effect of a preoperative telerehabilitation (PT) program on muscle strength, knee range of motion (ROM), and functional outcomes in candidates for total knee arthroplasty (TKA). Sixty patients (all women, mean age 70.53 ± 2.7 years) scheduled for bilateral TKA participated in this study. The PT and preoperative patient education (PE) groups participated in a 3-week intensive exercise program (30 min/session, 2 times/day, 5 days/week), whereas the control group received the usual care before TKA. Quadriceps muscle strength, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), ROM of knee flexion, pain, and time up-and-go (TUG) test time were evaluated at 4 weeks preoperatively, post-interventionally, and 6 weeks after TKA. Significant differences were found in the time-by-group interaction for 60°/s extension peak torque [F(4, 100) = 2.499, p = 0.047, η2p = 0.91], 180°/s extension peak torque [F(4, 100) = 3.583, p = 0.009, η2p = 0.125], ROM [F(4, 100) = 4.689, p = 0.002, η2p = 0.158], TUG time [F(4, 100) = 7.252, p < 0.001, η2p = 0.225], WOMAC pain [F(4, 100) = 9.113, p < 0.001, η2p = 0.267], WOMAC functional outcome [F(4, 100) = 6.579, p < 0.001, η2p = 0.208], and WOMAC total score [F(4, 100) = 10.410, p < 0.001, η2p = 0.294]. The results of this study demonstrate the early benefits of a PT program in elderly female patients with end-stage osteoarthritis. The PT program improved muscle strength, ROM, and functional outcomes before TKA, which contributed to better functional recovery after TKA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".