Comparative Effect of Graded Motor Imagery and Progressive Muscle Relaxation on Mobility and Function in Patients with Knee Osteoarthritis: A Pilot Study.
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is the most common musculoskeletal condition seen in aging. Joint destruction, chronic pain, change in proprioception, stability problems and decreased range of motion are the most common problems seen in OA. Complementary therapies like yoga, graded motor imagery (GMI), progressive muscle relaxation (PMR) and Tai Chi are more effective in chronic conditions such as knee OA. AIMS: The purpose of this study was to evaluate and compare the effect of graded motor imagery and progressive muscle relaxation on mobility and function in patients with knee OA. METHODS: This study was a randomized controlled pilot trial conducted in a tertiary health center in Belagavi, Karnataka, India. PARTICIPANTS: A total of 11 patients with unilateral knee pain persisting for more than 12 months were included in the study. INTERVENTIONS: Patients were randomly assigned to 2 groups: the PMR group (n = 5) or the (GMI) group (n = 6). Patients in the PMR group practiced Jacobson's PMR and patients in the GMI group practiced explicit and mirror therapy. All patients were treated 5 times a week for 2 weeks. OUTCOME MEASURES: The outcome measures in this study were range of motion and the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score for assessing knee joint pain, function and stiffness. RESULTS: Results demonstrated knee flexion range (P = .046) and function WOMAC scores (P = .0062) were significantly better in the GMI group than in the PMR group. CONCLUSION: GMI and PMR were both beneficial for knee mobility and function but GMI was better than PMR in chronic 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".