High-Intensity Laser Therapy (HILT) as an Emerging Treatment for Vulvodynia and Chronic Musculoskeletal Pain Disorders: A Systematic Review of Treatment Efficacy
Bibliographic record
Abstract
High-intensity laser therapy (HILT) has been gaining popularity in the treatment of chronic musculoskeletal pain, including vulvodynia. The objective of this study was to critically appraise and synthesize the available evidence on the efficacy of HILT for reducing pain and improving function in vulvodynia and other chronic primary musculoskeletal pain conditions. Electronic databases and the grey literature were searched. Effects on pain intensity, function, and adverse events were assessed. One study investigating HILT in the treatment of vulvodynia and 13 studies on the treatment of chronic musculoskeletal pain were selected. The study assessing vulvodynia showed favorable results for reducing pain. Regarding chronic musculoskeletal pain, 12 out of the 13 studies selected consistently showed that HILT was more effective than the placebo/active comparator for reducing pain and improving function. The available effect sizes for pain showed large to huge effects. Similar effects were observed for function except for two studies showing moderate effects. The GRADE score was moderate. Conclusions: There are insufficient data to support the use of HILT in vulvodynia, but the promising results encourage further research. HILT appears to be effective in musculoskeletal pain conditions. More high-quality studies are needed to identify effective laser protocols.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".