Utility of Screening for Blunt Vascular Neck Injuries with Computed Tomographic Angiography
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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