P.125 Value of CTneck angiography and expanded Denver Criteria in assessing blunt cerebrovascular injury (BCVI) in blunt cervical trauma, assaults, and strangulation
Bibliographic record
Abstract
Background: This study evaluates the utility of CT angiography (CTA) and the Expanded Denver Criteria (EDC) in detecting blunt cerebrovascular injuries (BCVI) in blunt cervical trauma, assaults, and strangulation. Methods: A retrospective review of 748 patients undergoing CTA for blunt cervical trauma, assaults, and strangulation (2013–2023) was conducted. After exclusions, 344 CTA reports were analyzed. Inclusion criteria: patients ≥18 years with complete medical records who underwent CTA for trauma evaluation. Exclusions: penetrating injuries, preexisting cerebrovascular abnormalities, incomplete records, or CTA not performed. Results: BCVI was identified in 38/344 cases (11%), with 55% classified as Grade I (Biffl). Posterior circulation (71%) and internal carotid arteries (36.8%) were most affected. Eight BCVI cases (21%) did not meet EDC; MVCs accounted for seven. MVCs (68%) and falls (29%) were the leading causes, while no BCVIs were observed in assaults or strangulations. Conclusions: MVCs and high-impact falls pose the highest BCVI risk, warranting CTA beyond EDC indications. In contrast, CTA may be less necessary for assaults and strangulations. Further studies across trauma centers are needed to confirm these findings.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".