Comparing the immediate and long-term effects of low and high power laser on the symptoms of knee osteoarthritis
Bibliographic record
Abstract
Background and purpose: Osteoarthritis is the most common type of arthritis. It is the main cause of chronic musculoskeletal pain and disability in elderly population. The aim of this research was to compare the effects of low-level laser therapy (LLLT) and high-intensity laser therapy (HILT) on pain relief and reducing disability in patients with knee osteoarthritis. Materials and methods: A total of 45 female patients participated in this randomized controlled study.The patients were randomly divided into three groups of low level laser, high power laser, and placebo laser. All patients, received standard treatment. Pain at rest and knee function were assessed by visual analog scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), respectively before, immediately, and six weeks after the intervention. Results: Low-power laser and high-power laser had immediate and long-lasting effect on reducing pain and disability (p<0.001). The immediate and lasting effect of these two interventions between the two groups were not significantly different (p>0.05). Conclusion: High power laser was found to have similar effects to low power laser. LLLT is believed to be more appropriate since it is more economical for both therapist and patient. (Clinical Trials Registry Number: IRCT201502224549N8)
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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) | 0.001 | 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) | 0.003 | 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".