Efficacy of tele-rehabilitation compared with office-based physical therapy in patients with knee osteoarthritis: A randomized clinical trial
Bibliographic record
Abstract
Introduction Knee osteoarthritis is a major cause of disability among the middle to senior age groups. Despite being effective, office-based physical therapy (OBPT) needs professional human resources and is both costly and time-consuming. We aimed to compare the efficacy of tele-rehabilitation (tele-rehab) compared with OBPT in patients with knee osteoarthritis. Methods In this randomized clinical trial, patients with symptomatic osteoarthritis of the knee were assigned to participate in either a 6-week home-based tele-rehab or an OBPT program between 2015 and 2016. Our primary outcome was the mean change from the baseline until 1 and 6 month's post-intervention in scores of the Knee injury and Osteoarthritis Outcome Score (KOOS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). We used analysis of variance for the repeated measure statistical test. Results A total of 54 patients entered the final analysis, with 27 in each group. The mean age of the patients was 58.2 ± 7.41 years and 60.2% were female. In the tele-rehab and OBPT group, KOOS scores increased from baseline to 6 months post-intervention (50.6 to 83.1 and 49.8 to 81.8) respectively. There was no significant difference between tele-rehab and OBPT groups in any of the studied scales. Discussion The tele-rehab program is as effective as OBPT in improving the function of patients with knee osteoarthritis. Considering the much lower time and cost consumed by tele-rehab, it is the recommended program for the older population living in remote sites.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".