Non-pharmacological Management of Soft Tissue Disorders of the Shoulder
Bibliographic record
Abstract
Objective: To develop an evidence‐based clinical practice guideline for the non‐pharmacological management of shoulder soft tissue disorders (shoulder pain). Methods: This guideline is based on seven systematic reviews. A multidisciplinary expert panel formulated recommendations based on evidence of effectiveness, safety, cost-effectiveness, societal and ethical values, and patient experiences (through qualitative research). Target audience includes clinicians; target population is adults with shoulder pain (sprains/strains, tendinopathies). Recommendations: When managing shoulder pain, clinicians should rule out major pathologies, assess prognostic factors for delayed recovery, offer education and reassurance, and provide care in partnership with the patient. For shoulder pain ≤3 months’ duration, clinicians may consider cervicothoracic manipulation and mobilization as adjunct to usual care, thoracic manipulation, multimodal care (heat/cold, mobilization, exercise), or low-level-laser therapy. For shoulder pain >3 months’ duration, clinicians may consider exercise, laser acupuncture, low-level-laser therapy, general practitioner care, thoracic manipulation, cervicothoracic manipulation and mobilization with usual care, or multimodal care (combining heat/cold, mobilization, exercise). Clinicians should not offer cervical mobilization as adjunct to multimodal care, cervicothoracic manipulation and mobilization as adjunct to exercise, multimodal care (combining exercise, mobilization, taping, psychological intervention, massage), shockwave therapy, ultrasound, taping, interferential current, diacutaneous fibrolysis, or massage. For calcific tendinitis, clinicians may consider shockwave therapy. Clinicians should reassess at every visit and determine whether discharge or a referral is indicated. Public health impact/implications: Our guideline provides evidence-based recommendations intended to optimize patient care, reduce inefficient practices and healthcare costs, and improve health outcomes related to shoulder pain. Our recommendations help guide shared decision-making with patients, bridge the gap between research and practice, and reduce variation in care among clinicians. Our guideline identifies interventions that may provide some benefit, little effect, or potential harm to assist policymakers with decision-making at the population level. Overall, this guideline contributes to preventing and limiting the burden of musculoskeletal disability.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".