What Are the Expected Findings on Follow-up Computed Tomography Angiogram in Post-traumatic Patients with Blunt Cerebrovascular Injury?
Bibliographic record
Abstract
PURPOSE: Blunt cerebrovascular injury (BCVI) is a rare but potentially devastating diagnosis. Our study establishes the temporal changes and findings on follow-up imaging. METHODS: For this retrospective, institutional review board-approved study, the hospital trauma registry was queried for all severely injured polytrauma patients who underwent computed tomography angiogram (CTA) scans in the emergency department between January 1, 2010, and December 31, 2016, with injury severity score ≥16, yielding 3747 patients. A total of 128 patients had a follow-up CTA for BCVI. The grade, location, and outcomes of injuries on follow-up imaging were studied. RESULTS: A vehicular collision was the most common mechanism of injury (75%). The majority of patients (61%) had a Glasgow Coma Scale of 10-15. Vertebral fractures were the most common associated injury (57%). The overall incidence of BCVI in our study population was 4.8%. On the initial CTA, 50% of injuries were grade 1, 25.4% were grade 2, 7% were grade 3, 17% were grade 4, and 0.6% were grade 5. For the different grades of injuries, improvement has been documented in 44% with complete healing in 34%, while 51% of injuries remained unchanged from the initial scan. Only 5% progressed to a higher-grade injury. Twelve patients developed strokes with an incidence of 9.4% in patients with a follow-up CTA. CONCLUSIONS: This study can help increase the awareness of radiologists about the evolution patterns of different grades of BCVIs on follow-up CTA for severely injured posttraumatic patients.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".