Efficacy of High-Intensity and Low-Level Laser Therapy Combined With Exercise Therapy on Pain and Function in Knee Osteoarthritis: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
Introduction: High-intensity laser therapy (HILT) and low-level laser therapy (LLLT) combined with exercise therapy (ET) have emerged as effective treatment options for musculoskeletal pain. However, there have remained uncertainties regarding the magnitude of their effects in reducing pain and improving function in patients with knee osteoarthritis. Hence, we performed a systematic review and network meta-analysis of available evidence in the literature to answer this query. Methods: A literature search was carried out in Embase, PubMed, and Scopus databases without any language restrictions from 1 January 1990 to 31 December 2023. We examined randomized controlled trial (RCT) studies that investigated the efficiency of HILT or LLLT plus knee osteoarthritis ET in pain and functional improvement of the knee. We performed a network meta-analysis and provided the standardized mean difference (SMD) with a 95% confidence interval (CI) by pooling the continuous data on the visual analogue scale (VAS) pain score and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) function score using a random-effects model. Results: In total, 11 eligible RCTs were included. Our analysis revealed significant improvements in the VAS pain and WOMAC function scores on weeks 4 and 8 after interventions in groups treated with LLLT+ET and HILT+ET compared with placebo+ET. Moreover, HILT+ET showed a greater reduction in the VAS pain score (SMD=-1.41; 95% CI: -2.05 to -0.76) and improvement in the WOMAC function score (SMD=-2.20; 95% CI: -3.21 to -1.19) than LLLT+ET in week 8. Conclusion: Based on our findings, both HILT+ET and LLLT+ET treatments effectively reduced pain and improved function, but HILT+ET showed a more significant improvement in both outcomes compared to LLLT+ET.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".