Efficacy of Balance Training in Combination With Physical Therapy in Rehabilitation of Knee Osteoarthritis: A Randomized Clinical Trial
Bibliographic record
Abstract
Objectives: The aim of this study was to assess the efficacy of balance training in combination with physical modalities in patients with knee OA. Materials and Methods: In this single-blinded randomized clinical trial, 60 patients (mean age: 56.5±0.90 years) with primary knee osteoarthritis were recruited. Participants were assigned to two groups. Physiotherapy (PT) group received routine physical therapy (hot pack, ultrasound, transcutaneous electrical nerve stimulation (TENS), and exercise) while balance training group (BT) received conventional physical therapy plus balance training using Biodex balance system (BBS). Results: The comparison between two groups revealed significant differences with regard to the visual analogue scale (VAS) pain score (P=0.023), Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain score (P=0.018), WOMAC total score (P=0.042), Lequesne index (P=0.015) as well as the score of Timed Up and Go (TUG) test (P=0.003). Similar results were observed for the fall risk score (P<0.001). The WOMAC stiffness score in the BT group significantly decreased from 2.80±0.34 to 1.70±0.32 (P<0.0 1), but it did not decrease in the PT group (P=0.096). Conclusions: The combination of balance training and physical therapy provided more pain relief and development of functional abilities in patients with knee OA.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".