The Effect of Telehealth-Based Rehabilitation on Patient Reported Outcomes and Objective Clinical Measurements in Patients With Degenerative Meniscal Tear
Bibliographic record
Abstract
Aim: The study aimed to compare the effectiveness of telehealth-based home exercises and conventionally prescribed home exercises in patients with degenerative meniscal tears. Material and Method: A two-armed, randomized controlled study was conducted with 49 participants with degenerative meniscal disease. Patients were randomized into Telerehabilitation (TR=25) and Conventional Home Exercise Rehabilitation (CR=24) groups. The TR group provided video exercises and self-management education via an online platform. The same protocol was given to the CR group in the clinical setting. Pain with Visual Analog Scale, muscle strength with Hand-Held Dynamometer, proprioception with Baseline bubble inclinometer, functional status with Western Ontario and McMaster Universities Osteoarthritis Index, exercise adherence with Exercise Adherence Rating Scale, quality of life with Short Form-12 were evaluated at baseline and after eight weeks of intervention. In addition, satisfaction and usability were assessed with the Telemedicine Satisfaction and Usefulness Questionnaire at week 8. Results: The TR group improved activity pain, proprioception, some parameters of the muscle strength outcomes, and exercise adherence scores (p0.05). In addition, 52 percent of the TR group reported high levels of satisfaction and usability. Conclusion: Despite increased participation and satisfaction, the telerehabilitation group noticed improvements in clinician-based measures (proprioception, strength) but not in rest pain, function, and quality of life. As a result, telerehabilitation-based home exercises prescribed to these patient groups are more effective.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".