Cerebral Vascular Accidents After Lumbar Spine Fusion
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort. OBJECTIVE: To determine the impact of a cerebral vascular accident (CVA) after lumbar spinal fusion, a population-based database was analyzed to identify the incidence, potential risk factors, hospital resource utilization, and the early postoperative outcomes. SUMMARY OF BACKGROUND DATA: A lumbar fusion (LF) is an effective surgical procedure to treat lumbar degenerative pathology. Although rare, a CVA can be a catastrophic event after an LF. METHODS: The Nationwide Inpatient Sample database was queried from 2002-2011. Patients undergoing an elective anterior lumbar fusion, a posterior lumbar fusion, or a combined anterior-posterior lumbar fusion were separated into subcohorts. Patients with a documented postoperative CVA were identified. Patient demographics, comorbidities (Charlson Comorbidity Index), length of stay, costs, early postoperative outcomes, and mortality were assessed. Statistical analysis involved T tests, χ2 analysis, and binary logistic regression with P < 0.001 denoting significance. RESULTS: A total of 264,891 LFs were identified between 2002 and 2011 of which 340 (1.3 per 1000) developed a postoperative CVA. Patients with a CVA were significantly older and demonstrated a greater comorbidity burden (Charlson Comorbidity Index). Patients with a CVA incurred a significantly greater length of stay, total hospital costs ($41,454 vs. $25,885), and a greater mortality rate (73.7 vs. 0.8 per 1000 patients). Regression analysis demonstrated that age more than 65 years and a history of neurological disorders, paralysis, congestive heart failure, or electrolyte imbalance were associated with an increased risk of a postoperative CVA. CONCLUSION: Patients who developed a postoperative CVA demonstrated a significantly greater incidence of postoperative complications, mortality, and total hospital costs. This study highlights important associated risk factors (e.g., age more than 65, neurological disorders, congestive heart failure) that may enable surgeons to identify high-risk patients prior to surgery. Further studies are warranted to characterize these risk factors and to establish guidelines to mitigate the complications associated with a postoperative CVA. LEVEL OF EVIDENCE: 4.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".