Photobiomodulation in the treatment of chronic non-specific neck pain. Randomized clinical trial
Bibliographic record
Abstract
Introduction Neck pain is defined as the presence of musculoskeletal pain in the posterior region of the neck, above the shoulders, or in the upper dorsal area. Physiotherapy aims to minimize pain, recover mobility, and strengthen muscles. For this, it uses several techniques, such as photobiomodulation, which can be achieved by light emitting diode (LED) therapy and low level laser therapy (LLLT). The objective of this study was to analyse the effect of the association of LED and LLLT in the treatment of chronic non-specific neck pain. Methods A quantitative, experimental, randomized study was performed. The sample was composed of 28 individuals, divided into a control group and an intervention group. Pre- and post-treatment visual analogue scale, Leeds Assessment of Neuropathic Symptoms and Signs, and the McGill Pain Questionnaire were used. Both groups were submitted to 6 sessions during 2 weeks, with a cluster apparatus, composed of an arrangement of 3 LEDs (590 nm, 1500 mW) and an LLLT (830 nm, 150 mW); the control group received placebo laser intervention. The application was punctual (1 minute per region), at the point of greatest pain, in the trapezius, scalene, and sternocleidomastoid muscles. Results In both cases, the pain reduction was significant (p < 0.05) for the 3 assessment instruments; however, the effect sizes for the visual analogue scale and the McGill Pain Questionnaire were higher in the intervention group. Conclusions The cluster used was effective in reducing pain in individuals with chronic non-specific neck pain.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".