Epidemiology and Outcomes of Vertebral Artery Injury in 16 582 Cervical Spine Surgery Patients: An AOSpine North America Multicenter Study
Bibliographic record
Abstract
Study Design: A multicenter retrospective case series was compiled involving 21 medical institutions. Inclusion criteria included patients who underwent cervical spine surgery between 2005 and 2011 and who sustained a vertebral artery injury (VAI). Objective: To report the frequency, risk factors, outcomes, and management goals of VAI in patients who have undergone cervical spine surgery. Methods: Patients were evaluated on the basis of condition-specific functional status using the Neck Disability Index (NDI), modified Japanese Orthopaedic Association (mJOA) score, the Nurick scale, and the 36-Item Short-Form Health Survey (SF-36). Results: VAIs were identified in a total of 14 of 16 582 patients screened (8.4 per 10 000). The mean age of patients with VAI was 59 years (±10) with a female predominance (78.6%). Patient diagnoses included myelopathy, radiculopathy, cervical instability, and metastatic disease. VAI was associated with substantial blood loss (770 mL), although only 3 cases required transfusion. Of the 14 cases, 7 occurred with an anterior-only approach, 3 cases with posterior-only approach, and 4 during circumferential approach. Fifty percent of cases of VAI with available preoperative imaging revealed anomalous vessel anatomy during postoperative review. Average length of hospital stay was 10 days (±8). Notably, 13 of the 14 (92.86%) cases resolved without residual deficits. Compared to preoperative baseline NDI, Nurick, mJOA, and SF-36 scores for these patients, there were no observed changes after surgery ( P = .20-.94). Conclusions: Vertebral artery injuries are potentially catastrophic complications that can be sustained from anterior or posterior cervical spine approaches. The data from this study suggest that with proper steps to ensure hemostasis, patients recover function at a high rate and do not exhibit residual deficits.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".