The Effectiveness of Adding High-Intensity Laser Therapy (HILT) to Physical Exercise in Reducing Pain, Improving Muscle Strength, and Enhancing Functional Ability in Patients with Knee Osteoarthritis: A Randomized Controlled Trial
Bibliographic record
Abstract
Introduction: Osteoarthritis (OA) is a leading cause of disability worldwide, impacting patient’s daily functional abilities as well as quality of life due to chronic pain associated with joint damage. High-Intensity Laser Therapy (HILT) has been proven effective in reducing pain and improving function in patients with knee OA. Despite its recognition as a safe and effective modality, standardized protocols for its use in knee OA are lacking. This study aims to evaluate the effectiveness of incorporating HILT in addition to physical exercise programs for patients with knee OA.Methods: 30 patients with knee OA were randomized into 2 groups, HILT + physical exercise (intervention group), and physical exercise only (control group). Visual Analogue Scale (VAS), quadriceps and hamstring muscle strength using exercise testing, and functional ability using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire were measured and compared.Result: Both groups displayed statistically significant improvement in pain, muscle strength, and functional ability by the end of the program (week 4) compared to the initial examination (p less than 0.05). When compared, there was a significant difference in pain reduction and functional ability in favor of the intervention group (p less than 0.05). However, there were no significant differences in quadriceps and hamstring muscle strength between the groups (p=0.148 and p=0.345, respectively).Conclusion: In this study, it was shown that the combination of HILT and physical exercise was more effective in alleviating pain and enhancing functional ability in patients with knee OA compared to physical exercise alone.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".