Efficacy of High-Power Laser Therapy versus Pulsed Electromagnetic Field Therapy in Knee Osteoarthritis
Bibliographic record
Abstract
Knee osteoarthritis (OA) is one of the common musculoskeletal conditions causing pain, disability, and low quality of life. Alternative methods to conventional treatment have been put forward, like high-power laser therapy (HPLT) and pulsed electromagnetic field (PEMF) therapy, but data obtained have proved unsatisfactory. Objectives: To compare and contrast the short-term outcomes of HPLT and PEMF therapy on the reduction of pain and functional improvement in patients with knee osteoarthritis. Methods: A quasi-experimental design was used which was carried out in Shiraz, Iran, among 40 patients with knee OA (20 in each group). Group A was accorded HPLT, and Group B was accorded PEMF therapy for all 10 sessions. Visual Analogue Scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were used in the pre-intervention and post-intervention measures to determine the intensity of pain and physical function, respectively. Data were compared through paired t-test and ANOVA with p<0.05 as a significant value. Results: VAS and WOMAC scores significantly (p<0.05) reduced in both groups after the treatment. Intergroup comparison also demonstrated a great difference in VAS reduction where HPLT was more effective in relieving pain, although there was no significant difference in the WOMAC scores of the two groups. Conclusions: HPLT and PEMF treatment are effective in decreasing pain and enhancing the function in knee OA. The therapies demonstrated that HPLT was better in pain relief, and functional improvements were the same.
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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 | 0.001 |
| 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 | 0.000 | 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".