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Record W4410122369 · doi:10.24908/pocusj.v10i01.17853

What is the Role of Point of Care Ultrasound for Suspected Pulled Elbow in Children?

2025· article· en· W4410122369 on OpenAlexvenueno aff
Salmah Lashhab, David McCreary

Bibliographic record

VenuePOCUS Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsElbowMedicineUltrasoundCINAHLRadiologyProspective cohort studyUltrasonographySurgeryPhysical therapyPsychological intervention

Abstract

fetched live from OpenAlex

Objective: Our objective was to evaluate and appraise the existing evidence on the use of point of care ultrasound (POCUS) for pulled elbow, including its positive findings and their reliability. Methods: We searched PubMed, Medline, EMBASE, CINAHL and Google Scholar for prospective and retrospective studies evaluating POCUS use for suspected pulled elbow. We identified positive sonographic findings along with their sensitivity and specificity relating to this diagnosis. Results: We included 13 studies that reviewed ultrasonographic findings in suspected pulled elbow. These studies discussed a range of sonographic findings between them, including radio- capitellar distance, 'J-sign'/'Hook sign', fat pad sign and partial eclipse sign. The studies were of mixed quality and were susceptible to bias. Conclusions: Children presenting with suspected pulled elbow who have evidence of hook sign (or J-sign) and an absence of elbow effusion on POCUS can be diagnosed with pulled elbow and safely undergo reduction. POCUS can be used following reduction to demonstrate resolution of these signs and confirm its success. Elbow injuries with effusion are likely to have bony injury, meaning that X-ray is required. Additional prospective study of children presenting with elbow injury would be required to accurately determine the effectiveness of POCUS in the diagnosis of pulled elbow.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.262
Teacher spread0.257 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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