A Systematic Review and Meta-Analysis of Vertebral Artery Injury After Cervical Spine Trauma
Bibliographic record
Abstract
STUDY DESIGN: Systematic Review and Meta-Analysis. OBJECTIVE: Identify the incidence, mechanism of injury, investigations, management, and outcomes of Vertebral Artery Injury (VAI) after cervical spine trauma. METHODS: A systematic review and meta-analysis were conducted in accordance with the PRISMA guidelines (PROSPERO-ID CRD42021295265). Three databases were searched (PubMed, SCOPUS, Google Scholar, CINAHL PLUS). Incidence of VAI, investigations to diagnose (Computed Tomography Angiography, Digital Subtraction Angiography, Magnetic Resonance Angiography), stroke incidence, and management paradigms (conservative, antiplatelets, anticoagulants, surgical, endovascular treatment) were delineated. Incidence was calculated using pooled proportions random effects meta-analysis. RESULTS: A total of 44 studies were included (1777 patients). 20-studies (n = 503) included data on trauma type; 75.5% (n = 380) suffered blunt trauma and 24.5% (n = 123) penetrating. The overall incidence of VAI was .95% (95% CI 0.65-1.29). From the 16 studies which reported data on outcomes, 8.87% (95% CI 5.34- 12.99) of patients with VAI had a posterior stroke. Of the 33 studies with investigation data, 91.7% (2929/3629) underwent diagnostic CTA; 7.5% (242/3629) underwent MRA and 3.0% (98/3629) underwent DSA. Management data from 20 papers (n = 475) showed 17.9% (n = 85) undergoing conservative therapy, anticoagulation in 14.1% (n = 67), antiplatelets in 16.4% (n = 78), combined therapy in 25.5% (n = 121) and the rest (n = 124) managed using surgical and endovascular treatments. CONCLUSION: VAI in cervical spine trauma has an approximate posterior circulation stroke risk of 9%. Optimal management paradigms for the prevention and management of VAI are yet to be standardized and require further research.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".