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Record W4311011847 · doi:10.1002/msc.1719

The major pain source of rotator cuff‐related shoulder pain: A narrative review on current evidence

2022· review· en· W4311011847 on OpenAlexaff
Chi Ngai Lo, Bernard P. Leung, Grant Sanders, Megan Wai Ming Li, Shirley P.C. Ngai

Bibliographic record

VenueMusculoskeletal Care · 2022
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsCanadian Chiropractic Association
Fundersnot available
KeywordsMedicineRotator cuffNarrative reviewNociceptionPhysical medicine and rehabilitationEtiologyPhysical therapyMechanism (biology)Neuropathic painAnesthesiaIntensive care medicineSurgeryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Rotator cuff-related shoulder pain (RCRSP) was proposed to have a complex pain mechanism, but the exact aetiology is still unclear. A recent review summarised the updated research to analyse the traditional concept of shoulder impingement which may not be accurate. Current studies have demonstrated that mechanical factors including a reduction in subacromial space, scapular dyskinesia and different acromial shapes are unlikely directly contributing to RCRSP. AIMS: Since the precise RCRSP pain mechanism remains unclear, the aim of this narrative review is to discuss possible sources of pain contributing to RCRSP according to the mechanisms-based pain classifications. RESULTS AND DISCUSSION: Research findings on potential mechanical nociceptive factors of RCRSP are conflicting; investigations of neuropathic and central pain mechanisms of RCRSP are limited and inconclusive. Overall, available evidence has indicated moderate to strong correlations between RCRSP and chemical nociceptive sources of pain. CONCLUSION: Results from current research may provide new directions for future studies on the aetiology of RCRSP and its clinical management towards a biochemical view instead of the traditional mechanical hypothesis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.060
GPT teacher head0.393
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2022
Admission routes1
Has abstractyes

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