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Record W3012160449 · doi:10.1177/0846537120909468

An Update in Imaging of Blunt Vascular Neck Injury

2020· review· en· W3012160449 on OpenAlexaff
Rawan Abu Mughli, Tong Wu, Jessica Li, Saba Moghimi, Zersenay Alem, Muhammad Umer Nasir, Waleed Abdellatif, Savvas Nicolaou

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2020
Typereview
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineBluntRadiologyComputed tomographyMedical imagingBlunt traumaCarotid arteriesMedical physicsSurgery

Abstract

fetched live from OpenAlex

Traumatic injuries of the cervical carotid and vertebral arteries, collectively referred to as blunt cerebrovascular injury (BCVI), can result in significant patient morbidity and mortality, with one of the most feared outcomes being cerebrovascular ischemia. Systematic imaging-guided screening for BCVI aims for early detection to guide timely management. In particular, accurate detection of the severity and grade of BCVI is paramount in guiding initial management. Furthermore, follow-up imaging is required to decide the duration of antithrombotic therapy. In this article, classification of the grades of BCVI and associated imaging findings will be outlined and diagnostic pitfalls and mimickers that can confound diagnosis will be described. In addition, updates to existing screening guidelines and recent efforts of criteria modification to improve detection of BCVI cases will be reviewed. The advent of postprocessing tools applied to conventional computed tomography (CT) angiograms and new diagnostic tools in dual energy CT for improved detection will also be discussed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.299
Teacher spread0.281 · 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 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

Citations17
Published2020
Admission routes1
Has abstractyes

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