Applications of Photobiomodulation Therapy to Musculoskeletal Disorders and Osteoarthritis with Particular Relevance to Canada
Bibliographic record
Abstract
Background: Musculoskeletal disorders caused by osteoarthritis (MSDs/OA) are a growing problem in the modern industrialized society in Canada. Overall aging of the general population and a progressive lack of exercise contribute to this alarming increase. Moreover, a range of chronic conditions including cardiovascular and mental diseases show significantly higher comorbidity with MSDs/OA. Conventional medical treatment for MSDs/OA includes nonsteroidal anti-inflammatory drugs and opiate pain killers. These drugs have major drawbacks such as a relative lack of efficacy, potential for addiction, and even death (Vioxx scandal). Photobiomodulation (PBM) was discovered over 50 years ago but has still not attained widespread acceptance by the medical community. This is partly due to uncertainty about the precise molecular mechanisms of action and a bewildering array of different wavelengths and dosimetric parameters employed in reported studies. Objective: The goal of this review was to survey literature reports of PBM, also known as low-level laser therapy used for treatment of MSDs/OA, concentrating on the growth over time, different wavelengths employed, and application to different joints. Methods: We searched the PubMed database for publication of study on PBM to treat the most common joints. Results: We show that the field of PBM to treat MSDs/OA is expanding exponentially over the past 20 years. A trend has emerged over time for more power to achieve better effective treatments, and the understanding of the physiological effect of safe parameters has improved. There is, however, no consensus on the best set of parameters to treat a specific patient indication. Conclusions: Finally, we highlight gaps in our knowledge and the barriers to further clinical trials. We suggest that the growing body of evidence indicating efficacy, and the almost total lack of side effects, should encourage continued clinical research to support clinical applications where better rehabilitation treatments are much needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".