Rates of healing and timing of repeat imaging after blunt cerebrovascular injury: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Blunt cerebrovascular injury (BCVI) is a nonpenetrating carotid and/or vertebral artery injury following trauma. Treatment typically involves antiplatelets or anticoagulation followed by repeat imaging. However, little is known regarding the natural history of BCVI on treatment. Therefore, we performed a systematic review and meta-analysis to summarize the healing rates at various intervals of repeat imaging. METHODS: We searched Embase and Medline from inception to November 22, 2024. We included studies reporting imaging-based follow-up outcomes of adult patients with BCVI. We organized data based on injury status and summarized overall resolution, progression, stability, and worsening of BCVI at various time points and according to injury grade. RESULTS: We included 20 studies involving 2,641 patients. Studies were predominantly retrospective in nature, originating from North America, and follow-up was primarily performed using computed tomography angiography. The median (Q1 to Q3) stroke incidence was 8.5% (5.1% to 13.1%). We demonstrate that lower-grade injury is associated with BCVI healing at follow-up imaging (pooled unadjusted odds ratio, 6.73; 95% confidence interval, 4.23-10.71, moderate certainty). In addition, we demonstrate that Grades I and II injuries demonstrated higher rates of resolution or improvement at every follow-up imaging period. CONCLUSION: This review demonstrates with moderate certainty that lower-grade BCVIs probably heal faster, while higher-grade BCVIs persist longer. These findings emphasize the importance of considering injury grade when determining the appropriate follow-up imaging interval. LEVEL OF EVIDENCE: Systematic Review and Meta-analysis; Level IV.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".