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Record W4406189298 · doi:10.1136/emermed-2024-214540

Detection of paediatric skull fractures using POCUS

2025· review· en· W4406189298 on OpenAlexaff
Hamza Shogan, Avneesh Kumar Bhangu

Bibliographic record

VenueEmergency Medicine Journal · 2025
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineSkullSkull fractureStudy TypeAccident and emergencyPoint of care ultrasoundHead injurySurgeryEmergency medicineUltrasoundRadiologyMedical emergency

Abstract

fetched live from OpenAlex

A shortcut review of the literature was conducted to examine the sensitivity and specificity of point-of-care ultrasound (POCUS) in detecting paediatric skull fractures. A total of 162 publications were screened by title and abstract, 13 studies underwent full text review, and after review of bibliographies of meta-analyses and systematic reviews, a total of 6 articles were included. Details about the author, date of publication, country of publication, patient group studied, study type, relevant outcomes (skull fracture), results and study limitations were tabulated. The clinical bottom line is that, in paediatric patients with a minor head injury, POCUS performed by emergency medicine physicians has a sensitivity ranging between 77% and 100% and a specificity between 85% and 100% for skull fracture detection, and its use in clinical decision-making has yet to be validated.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.000

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 head0.103
GPT teacher head0.475
Teacher spread0.372 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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