RANDOMIZED TRIAL COMPARING AI-TAILORED HOME PHYSIOTHERAPY VERSUS CLINIC-BASED REHAB IN KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) is a prevalent degenerative joint condition and a leading cause of pain and disability worldwide. While physiotherapy is a cornerstone of non-surgical OA management, barriers to accessing clinic-based care often reduce adherence and limit outcomes. Technological innovations such as artificial intelligence (AI) offer a novel solution for delivering personalized, home-based rehabilitation. Objective: To compare the effectiveness of AI-tailored home physiotherapy with traditional clinic-based rehabilitation in improving function, reducing pain, and enhancing satisfaction among patients with knee OA. Methods: A 12-month, single-blind randomized controlled trial was conducted in Lahore, Pakistan, with 144 participants aged 45–70 years diagnosed with grade II–III knee OA. Participants were randomly assigned to either an AI-driven home physiotherapy group or a standard clinic-based rehabilitation group (n = 72 per group). Outcomes were measured at baseline, 6 weeks, and 12 weeks using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Visual Analog Scale (VAS) for pain, Timed Up and Go (TUG) test, and patient satisfaction ratings. Statistical analyses included repeated-measures ANOVA and independent t-tests with significance set at p < 0.05. Results: Participants in the AI group showed significantly greater improvements in WOMAC scores (58.6 ± 6.9 to 29.5 ± 6.2) and VAS scores (7.2 ± 1.0 to 3.1 ± 1.1) compared to the clinic group (p < 0.01). TUG test times and patient satisfaction ratings also favored the AI intervention. No adverse events were reported. Conclusion: AI-tailored home physiotherapy is a clinically effective and patient-preferred alternative to conventional rehabilitation for knee OA, offering scalable benefits for enhancing access and outcomes in musculoskeletal care.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".