UTILIZATION OF PULSED ELECTROMAGNETIC FIELD AND TRADITIONAL PHYSIOTHERAPY IN KNEE OSTEOARTHRITIS MANAGEMENT
Bibliographic record
Abstract
Background and Objectives: Pulsed Electromagnetic Field (PEMF) has been suggested as a treatment method for musculoskeletal system disorders. The present study was conducted to determine whether the addition of PEMF to traditional physical program produces better clinical outcomes than traditional physical program alone in the management of moderate knee osteoarthritis (OA). Design: A single-blinded, randomized controlled study Methods: Twenty subjects (5 men and 15 women) with unilateral moderate knee OA (Kellgren-Lawrence criteria grade 2). They were randomly allocated in 2 groups to receive: group (A) PEMF plus ultrasound plus exercises; or (B) ultrasound plus exercises. Both groups received the respective treatments 3 times per week for 4 weeks and underwent the same pretreatment and post treatment evaluation that included active knee range of motion (ROM) by universal goniometer, knee pain score by visual analogue scale (VAS) and knee functional performance by Western Ontario and McMaster Universities osteoarthritis index (WOMAC). Result: There was an improvement in both groups in active knee flexion ROM, reduced VAS score and improved WOMAC index , however, all outcomes were significantly better in group (A) (p <0.05). Moreover, the percentages of outcomes improvement were in favor of group (A). Conclusion: The addition of PEMF to traditional physical program in managing OA produced a greater improvement in pain relief, range of motion and resulted in better functional performance than did traditional physical program alone. The improvement in current study should be limited to short term outcomes of PEMF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".