The effects of laser therapy on pain, functionality and biomechanical parameters in patients suffering from gonarthrosis: an observational study.
Bibliographic record
Abstract
Background: Knee osteoarthritis (KOA) consists of a heterogeneous pathology of the peripheral joints, characterized by a complex, multifactorial nature and with multiple risk factors. The aim of this study was to evaluate the effects of laser therapy on pain and biomechanical parameters in patients with KOA. Methods: A retrospective study was carried out for patients with KOA (I-II Kellgren-Lawrence). All patients underwent 10 sessions of Laser therapy (three/week). Patients were evaluated at T0 and 22 days later-T1. At T0, before the treatment and at T1 pain (Visual Analogue Scale-VAS), symptoms (Knee Injury and Osteoarthritis Outcome Score-KOOS and Western Ontario and McMaster Universities Osteoarthritis Index-WOMAC) and instrumental assessment (gait analysis) were assessed. Results: Twelve patients were included. A significant difference was found in all three scales between T0-T1, VAS decreased by three points (p=0.002), KOOS increased by fifteen points (p=0.008) and WOMAC decreased by 8.5 (p=0.003). For gait analysis parameters, we detected a significant decrease in stance duration (p=0.04), a marked increase in speed normalized to height (p=0.01), an increase in knee ROM (p=0.01) and an increase in maximum knee moment (p=0.02). Conclusions: Laser therapy is an effective intervention in the management of KOA. Our study showed that laser therapy is effective in relieving pain and improving symptoms as well as gait parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".