Utility of Screening for Blunt Vascular Neck Injuries with Computed Tomographic Angiography
Bibliographic record
Abstract
PURPOSE: To prospectively study the impact of implementing a computed tomographic angiography (CTA)-based screening protocol on the detected incidence and associated morbidity and mortality of blunt vascular neck injury (BVNI). METHODS: Consecutive blunt trauma patients admitted to a single tertiary trauma center and identified as at risk for BVNI underwent admission CTA using an eight-slice multi-detector computed tomography scanner. The detected incidence, morbidity, and mortality rates of BVNI were compared with those measured before CTA screening. A logistic regression model was also applied to further evaluate potential risk factors for BVNI. RESULTS: A total of 1,313 blunt trauma patients were evaluated. One hundred seventy screening CTAs were performed, of which 33 disclosed abnormalities. Twenty-three were evaluated angiographically, of which 15 were considered to have significant BVNIs, as were 4 of the 10 patients with abnormal CTAs and no angiogram. The incidence of angiographically proven BVNIs in our series was 1.1%. If four patients who were treated for BVNIs based on CTA alone are included, the incidence rises to 1.4%. This is significantly higher than the 0.17% incidence before screening (p < 0.001). In addition, the delayed stroke rate and injury-specific mortality fell significantly from 67% to 0% (p < 0.001) and 38% to 0% (p = 0.002), respectively. Overall mortality also fell significantly, from 38% to 10.5% (p = 0.049). Univariate logistic regression identified the presence of cervical spine injury as a significant predictor of BVNI (p < 0.001). CONCLUSION: CTA screening increases the detected incidence of BVNI 8-fold, with rates similar to angiographically based screening protocols. CTA screening significantly decreases BVNI-related morbidity and mortality in an efficient manner, underlying its utility in the early diagnosis of this injury.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".