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Record W4395046270 · doi:10.24908/pocus.v9i1.16831

Point of Care Ultrasound Diagnosis of Maxillary Artery Pseudoaneurysm in the Emergency Department

2024· article· en· W4395046270 on OpenAlexvenueno aff
Stephen Alerhand, Marwa Ali, Sean Beckman

Bibliographic record

VenuePOCUS Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicVascular Procedures and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsPseudoaneurysmMedicineEmergency departmentRadiologyHematomaAngiographyComputed tomography angiographyLumen (anatomy)Intravascular ultrasoundAneurysmSurgery

Abstract

fetched live from OpenAlex

A pseudoaneurysm results from a tear in a vessel wall. This leads to extravasation of blood into adjacent tissue and eventual formation of a fibrous sac that maintains continuity with the lumen. These vascular injuries very rarely occur in deeper vessels of the face (e.g. maxillary artery) due to protection from structures like the bony mandible and parotid gland. If left untreated, these pseudoaneurysms can lead to infection, thromboembolism, hemorrhage, and compression of surrounding structures such as facial nerve branches. Pseudoaneurysms are typically diagnosed by advanced imaging techniques including computed tomography angiography and magnetic resonance angiography. However, these tests require time to perform and interpret, are costly, and take place outside the patient care area. Computed tomography also confers ionizing radiation. Fortunately, point of care ultrasound (POCUS) is a readily available, dynamic imaging tool that can be performed at the bedside. Here we present the first known case report of a maxillary artery pseudoaneurysm diagnosed by POCUS in the emergency department. Early differentiation from a typical hematoma led to rapid management in the form of a compression bandage, as well as expedited consultation to the appropriate services.

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 score0.094
Threshold uncertainty score0.286

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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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
Published2024
Admission routes1
Has abstractyes

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