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Record W4385576797 · doi:10.3390/diagnostics13152601

Infraspinatus Fascial Dysfunction as a Cause of Painful Anterior Shoulder Snapping: Its Visualization via Dynamic Ultrasound and Its Resolution via Diagnostic Ultrasound-Guided Injection

2023· article· en· W4385576797 on OpenAlexaff
King Hei Stanley Lam, Daniel Su, Yung‐Tsan Wu, Mario Fajardo Pérez, Kenneth Dean Reeves, Philip Peng, Bradley D. Fullerton

Bibliographic record

VenueDiagnostics · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineFasciaUltrasoundFascia lataAnterior shoulderDeep fasciaAnatomySurgeryRadiology

Abstract

fetched live from OpenAlex

This report presents the first case of painful anterior shoulder snapping due to a thickened, fibrotic bursa snapping between the subscapularis and the short head of the bicep during external and internal rotation of the humerus. A 46-year-old presented with a 10-month history of on-and-off anterolateral right shoulder pain and snapping. Direct treatment to the anterior suspected lesions partially and temporarily relieved the pain but did not reduce the snapping. Further musculoskeletal examination and dynamic ultrasound scanning showed dysfunction in the scapulothoracic movement and defects of the muscles that interact with the infraspinatus aponeurotic fascia. An ultrasound-guided diagnostic injection to the suspected lesions in the infraspinatus fascia and its muscles attachments improved the scapulothoracic movement, and the snapping and pain were eliminated immediately after the injection, which further shows that the defects in the infraspinatus fascia may be the root cause of the painful anterolateral snapping. The importance of the infraspinatus fascia and its related muscle in maintaining the harmony of the scapulothoracic movement and flexibility of the shoulder is considerable.

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)0.000
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2023
Admission routes1
Has abstractyes

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