The Effects of High Intensity Nd:YAG Laser on Pain and Function in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Background: Knee osteoarthritis (KOA) is the most common musculoskeletal disorder which often targets elderly. The aim of this study was to evaluate the effects of the high-intensity Nd:YAG laser therapy (HILT) compared to conventional physiotherapy (CPT) on pain and function in patients with knee osteoarthritis. Methods: In this randomized clinical trial study conducted in 2016-2017, sixty eligible patients with knee osteoarthritis were randomly assigned into two groups of high-intensity Nd:YAG laser therapy and conventional physiotherapy [including ultrasonography and transcutaneous electrical nerve stimulation (TENS)]. They received ten sessions of treatment. All patients were evaluated for pain intensity, range of knee flexion, functional tests, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire at the baseline and after the treatment. Wilcoxon and paired t tests were used to compare intragroup changes pre and post-intervention, and Mann-Whitney and independent sample t tests were used to compare between-group changes. Findings: There were significant differences in all variables (P < 0.001) between pre and post-intervention in each group. There were also significant differences in the pain (P < 0.001), and function subscale (P = 0.006) and total score (P = 0.030) of Western Ontario and McMaster Universities Osteoarthritis Index. Conclusion: The results of the study indicate that high-intensity Nd:YAG laser therapy is more effective in decreasing pain and increasing functional performance in comparison to conventional physiotherapy in patient with knee osteoarthritis.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".