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Record W3158689225 · doi:10.1002/ejp.1788

Noninvasive management of soft tissue disorders of the shoulder: A clinical practice guideline from the Ontario Protocol for Traffic Injury Management (OPTIMa) collaboration

2021· review· en· W3158689225 on OpenAlexaffabout
Hainan Yu, Pierre Côté, Jessica J. Wong, Heather M. Shearer, Silvano Mior, Carol Cancelliere, Kristi Randhawa, Arthur Ameis, Linda Carroll, Margareta Nordin, Sharanya Varatharajan, Deborah Sutton, Danielle Southerst, Craig Jacobs, Maja Stupar, Anne Taylor‐Vaisey, Douglas P. Gross, Robert J. Brison, Mike Paulden, Carlo Ammendolia, J. David Cassidy, Shawn Marshall, Richard N. Bohay, John Stapleton, Michel Lacerte

Bibliographic record

VenueEuropean Journal of Pain · 2021
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversité de MontréalUniversity of SaskatchewanOpen Text (Canada)University of OttawaQueen's UniversityProvincial Laboratory of Public HealthUniversity of AlbertaCanadian Nuclear Safety CommissionKingston General HospitalInstitute for Work & HealthCanadian Memorial Chiropractic CollegeOntario Tech UniversityPublic Health OntarioCentre for Disability Prevention and RehabilitationWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsGuidelineProtocol (science)MedicineSoft tissueSoft tissue injuryClinical PracticePhysical medicine and rehabilitationPhysical therapySurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: Objective of this study is to develop an evidence-based guideline for the noninvasive management of soft tissue disorders of the shoulder (shoulder pain), excluding major pathology. METHODS: This guideline is based on high-quality evidence from seven systematic reviews. Multidisciplinary experts considered the evidence of effectiveness, safety, cost-effectiveness, societal and ethical values, and patient experiences when formulating recommendations. Target audience is clinicians; target population is adults with shoulder pain. RESULTS: When managing patients with shoulder pain, clinicians should (a) rule out major structural or other pathologies as the cause of shoulder pain and reassure patients about the benign and self-limited nature of most soft tissue shoulder pain; (b) develop a care plan in partnership with the patient; (c) for shoulder pain of any duration, consider low-level laser therapy; multimodal care (heat/cold, joint mobilization, and range of motion exercise); cervicothoracic spine manipulation and mobilization for shoulder pain when associated pain or restricted movement of the cervicothoracic spine; or thoracic spine manipulation; (d) for shoulder pain >3-month duration, consider stretching and/or strengthening exercises; laser acupuncture; or general physician care (information, advice, and pharmacological pain management if necessary); (e) for shoulder pain with calcific tendinitis on imaging, consider shock-wave therapy; (f) for shoulder pain of any duration, do not offer ultrasound; taping; interferential current therapy; diacutaneous fibrolysis; soft tissue massage; or cervicothoracic spine manipulation and mobilization as an adjunct to exercise (i.e., range of motion, strengthening and stretching exercise) for pain between the neck and the elbow at rest or during movement of the arm; (g) for shoulder pain >3-month duration, do not offer shock-wave therapy; and (h) should reassess the patient's status at each visit for worsening of symptoms or new physical, mental, or psychological symptoms, or satisfactory recovery. CONCLUSIONS: Our evidence-based guideline provides recommendations for non-invasive management of shoulder pain. The impact of the guideline in clinical practice requires further evaluation. SIGNIFICANCE: Shoulder pain of any duration can be effectively treated with laser therapy, multimodal care (i.e., heat/cold, joint mobilization, range of motion exercise), or cervicothoracic manipulation and mobilization. Shoulder pain (>3 months) can be effectively treated with exercises, laser acupuncture, or general physician care (information, advice, and pharmacological pain management if necessary).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.447
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations19
Published2021
Admission routes2
Has abstractyes

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