Review of Multidetector Computed Tomography Angiography as a Screening Modality in the Assessment of Blunt Vascular Neck Injuries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Blunt vascular neck injuries (BVNI), previously thought to be rare, have demonstrated increasing incidence rates in recent literature and are associated with significant mortality and morbidity. A radiologist needs to efficiently recognize these injuries on preliminary screening to enable initiation of early management. When initiation of accurate management is started promptly, decreased rates of postinjury complications, for example, stroke, have been demonstrated. This article reviews the incidence, pathophysiology, and rationale for screening for these BVNI injuries. The utility of computed tomography angiography (CTA) as the potential new criterion standard as the screening and follow-up imaging modality for BVNI will be discussed. The application of new multidetector CTA techniques available, such as dual-energy CT and iterative reconstruction, are also reviewed. In addition, the characteristic imaging findings on CTA and the associated Denver Grading scale for BVNI will be reviewed to allow readers to become familiar with the injury patterns and to understand the prognostic and clinical implications, respectively. Examples of the spectrum of injuries, potential injury mimics, and different artifacts on multidetector CTA are shown to help familiarize readers and allow them to successfully and confidently recognize a true BVNI.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it