TO FIND OUT THE EFFECTIVENESS OF NMES & EXERCISE ALONG WITH PATIENT EDUCATION IN OSTEOARTHRITIS
Bibliographic record
Abstract
Osteoarthritis (OA) of the knee is a prevalent musculoskeletal condition that causes pain, stiffness, and functional limitations, significantly affecting the quality of life of those impacted. This study aimed to evaluate the effectiveness of a combined rehabilitation approach involving Neuromuscular Electrical Stimulation (NMES), isometric knee exercises, and patient education in the management of chronic knee OA. A total of 100 participants were randomly assigned to two groups: Group A received NMES therapy, isometric knee exercises, and education, while Group B received conventional treatment with isometric knee exercises and education. The outcomes were assessed using the Visual Analog Scale (VAS) for pain and the WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) for functional disability at both pre-treatment and post-treatment stages. The results demonstrated significant improvements in both pain reduction and functional mobility in both groups, with Group A showing more pronounced improvements compared to Group B. NMES combined with isometric exercises and education was found to be an effective intervention in improving pain, muscle strength, and function in knee OA patients. These findings suggest that incorporating NMES into rehabilitation programs for knee OA can offer additional benefits in the management of this chronic condition. Further research is required to explore the long-term effects and optimal protocols for these interventions.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".