Point of Care Ultrasound Diagnosis of Maxillary Artery Pseudoaneurysm in the Emergency Department
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".