Nonpharmacological Treatments for Musculoskeletal Pain
Bibliographic record
Abstract
BACKGROUND: Several types of physical therapy are used in the management of painful musculoskeletal disorders. These treatment modalities can be broadly categorized as electrotherapy modalities (e.g., transcutaneous electrical nerve stimulation), acupuncture, thermal modalities (e.g., moist heat, ultrasound), manual therapies (e.g., manipulation or massage), or exercise. Within each of these broad categories significant variations in treatment parameters are possible. OBJECTIVE: To consider the evidence base for each of these main categories of physical therapy in the management of musculoskeletal pain. METHOD: To consider the available evidence related to clinical effectiveness and then to review evidence from basic science studies evaluating potentially therapeutic effects of the various therapies. RESULTS: There seems to be evidence from basic science research to suggest that many of the therapies could have potentially therapeutic effects. However, there appears to be limited high-quality evidence from randomized clinical trials to support the therapeutic effectiveness of several of the therapies. CONCLUSIONS: There is some preliminary evidence to support the use of manual therapies, exercise, and acupuncture in the management of some categories of musculoskeletal pain. Limitations of the existing research base are discussed and recommendations for areas of future research are provided.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.009 | 0.003 |
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".