Low-level Laser Therapy in Knee Osteoarthritis: A Prospective Analytical Study
Bibliographic record
Abstract
Background/Aims: Osteoarthritis (OA) is the second common rheumatologic disorder and the most prevalent joint disease in India, affecting 20–40% of the population. Low-level laser therapy (LLLT) has been used to alleviate pain in musculoskeletal conditions. Despite the fact that LLLT is extensively used, the consequences from both experimental and medical research continue to be inconsistent. This study aims to assess the pain-relieving effectiveness of LLLT in patients with Kellgren–Lawrence Grade I and II knee OA, using the Visual Analog Scale (VAS) and the Western Ontario McMaster Osteoarthritis Index (WOMAC). Materials and Methods: The study was carried out at a tertiary care center in Bengaluru. Fifty patients were recruited primarily based on the following inclusion criteria: 1. Idiopathic knee OA 2. Grade I or II bilateral knee OA confirmed by X-ray 3. Average pain intensity of 40 or greater on a 100-mm VAS 4. Age: 45–65 years, of both sexes. Patients attended weekly therapy sessions. Each session included isometric quadriceps muscle contractions and 10 repetitions of active range of motion exercises for the knee joint, following a 5-min LLLT application. Results: On X-ray, 52% (22 patients) had Grade I OA knee, and 48% (20 patients) had Grade II OA knee. There was a significant reduction in VAS and WOMAC post-treatment scores compared to pre-treatment. Conclusion: OA is a long-term, degenerative condition that causes deterioration of joint tissues, resulting in excessive pain, stiffness, and restricted mobility. Treatment strategies for OA continue to be crucial for research. Our study suggests a widespread improvement in pain alleviation with LLLT. Further studies are needed to compare the efficacy of LLLT with other pain management strategies and explore combined treatment plans. Keywords: Low-level Laser, OA knee, VAS, WOMAC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".