Efficacy of different doses of high intensity laser and traditional exercise on pain and function in chronic knee osteoarthritis
Bibliographic record
Abstract
Introduction. High-intensity laser therapy (HILT) appears to be effective for knee osteoarthritis (KOA). However, no recommendations exist for the optimal dosage of HILT in chronic KOA. Aim of the study. This study examined how different dosages of HILT affect KOA pain and function. Materials and methods. Fifty-one patients with third-degree knee osteoarthritis, aged 50–70 years, were randomly assigned to three equal groups. Group A received exercises and a dose of 1500 J of HILT. Group B received exercises and a dose of 3000 J of HILT. Group C received sham laser treatment along with exercises. Pain and function were evaluated using the numerical pain rating scale (NPRS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Groups were evaluated before and after a six-week treatment period. Results. A mixed design multivariate analysis of variance (MANOVA) showed that pain and function significantly improved in all three groups after treatment (p < 0.001). Significant differences were observed between both active groups (A and B) and the control group. Between groups A and B, no significant differences were found (p > 0.05). However, descriptive analysis revealed that group B achieved greater improvements than group A. Conclusion. In patients with chronic KOA, HILT at either 1500 J or 3000 J combined with exercises effectively reduced pain and improved function. To save time and energy, a dosage of 1500 J may be recommended.
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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) | 0.000 | 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 | 0.000 | 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".