Blunt Vascular Neck Injuries: Diagnosis and Outcomes of Extracranial Vessel Injury
Bibliographic record
Abstract
BACKGROUND: Blunt vascular neck injuries (BVNIs) are rare, often occult, and potentially devastating injuries. The purpose of this study was to identify a high-risk group, which would benefit from screening. METHODS: Patients with BVNIs were identified from our trauma registry and charts were reviewed. Potential risk factors for BVNI were evaluated by univariate and multivariate logistic regression. RESULTS: Thirty-one BVNIs were identified in 22 patients. The stroke rate was 60% and the mortality rate was 25%. Univariate analysis showed Glasgow Coma Scale score < or = 8, head injury (Abbreviated Injury Scale [AIS] score > or = 3), basal skull fracture, facial injury, other neck injury, thorax injury (AIS score > or = 3), abdominal injury, and cervical spine injury to be significant (p < 0.05). The multivariate predictive model had two predictors remaining significant: thorax injury (AIS [thorax] score > or = 3) and Glasgow Coma Scale score < or = 8. CONCLUSION: Screening should be undertaken for patients at increased risk for BVNI: those with risk factors identified in our regression analysis and factors previously reported.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".