Blunt Cerebrovascular Injuries: Diagnosis and Management Outcomes
Bibliographic record
Abstract
BACKGROUND: Blunt cerebrovascular injury (BCVI) to the carotid and vertebral arteries is a potentially devastating injury in trauma patients. The optimal management for BCVI has not been standardized. At our institution, 64-slice multi-detector computed tomographic angiography (CTA) has been used as the initial screening exam for BCVI in patients who met predefined screening criteria. The purpose of this study is to review the incidence of CTA-diagnosed BCVI in at-risk patients and to evaluate the treatment and clinical outcome of patients with BCVI. METHODS: This study included trauma patients with a positive diagnosis of BCVI on CTA during a 41-month study period. The medical records and relevant radiographic findings were retrospectively reviewed. RESULTS: Twenty seven of 222 blunt trauma patients evaluated with CTA had a positive diagnosis of BCVI, with an occurrence rate of 12.2%. Traumatic brain injury (72.2%) and basal skull fractures (55.6%) were the most frequent associated injuries with carotid trauma while 100% of blunt vertebral injuries occurred in the setting of cervical fractures. Fourteen (51.8%) patients received medical therapy; Eleven (40.7%) patients received conservative treatment. Endovascular treatment was attempted in a single case of vertebral arteriovenous fistula. BCVI-related stroke was found in four patients (14.8%), one of whom developed an infarct while on medical treatment. CONCLUSIONS: BCVI is found in a significant portion of blunt trauma patients with identifiable risk factors, and screening CTA has high diagnostic yield in detecting these lesions. Medical therapy is the mainstay of treatment at our institution; however, BCVI-related stroke may occur despite treatment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".