Management of Penetrating Cerebrovascular Injuries in Pediatric Trauma: A Retrospective Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Blunt cerebrovascular injury is uncommon in the pediatric population; penetrating cerebrovascular injuries are even rarer and are thus poorly understood. OBJECTIVE: To describe the diagnosis and management of penetrating cerebrovascular injuries and describe outcomes of available treatment modalities. METHODS: Clinical and radiographic data were collected retrospectively from a multicenter trauma registry for children screened for cerebrovascular injury during 2003 to 2013 at 4 academic pediatric trauma centers. RESULTS: Among 645 pediatric patients evaluated with computed tomography angiography with blunt cerebrovascular injury, 130 also had a penetrating trauma indication. Seven penetrating cerebrovascular injuries were diagnosed in 7 male patients (mean age 12.4 years, range 12-18 years). Focal neurological deficit and concomitant intracranial injury were each seen in 2 patients. There were 2 intracranial carotid artery injuries, 4 extracranial carotid artery injuries, and 1 vertebral artery injury. The majority of injuries were higher than grade I (5/7; 71%): 2 were grade I, 1 grade II, 2 grade III, and 2 grade IV. The 2 patients with grade III injuries required open surgery, and 1 patient with a grade IV injury underwent endovascular treatment. Two patients suffered immediate stroke secondary to the penetrating cerebrovascular injury. There were no delayed neurological deficits from the penetrating injuries, and no patients died as a result of the injuries. CONCLUSION: This is the largest series of penetrating cerebrovascular trauma in the pediatric literature. Although rare, penetrating cerebrovascular injuries can be high-grade injuries that require urgent recognition and may require aggressive endovascular and/or open surgery for 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".