Endovascular Stent Grafts for Acute Blunt Aortic Injury
Bibliographic record
Abstract
BACKGROUND: Endovascular stent grafting (EVSG) has emerged as a new treatment for aortic disease and has recently been applied to the treatment of acute blunt aortic injury (BAI). The purpose of this study was to determine the outcome of EVSG for patients with BAI at two tertiary (Level I) trauma centers. METHODS: A retrospective review of patients treated between January 1, 1999, and February 1, 2003, at two centers, Calgary Health Region (Calgary, Alberta) and Harborview Medical Center (Seattle, WA), with EVSG for acute BAI is reported. Variables assessed included age, sex, Injury Severity Score, total length of stay (LOS), intensive care unit LOS, operative technique, complications, technical success, mortality, and follow-up data. RESULTS: Twenty-eight patients treated with EVSG for BAI were identified during this period. Twelve patients were excluded because injuries occurred more than 30 days before grafting (n = 7) or under a different protocol (n = 4), or the procedure was performed in a different center (n = 1). Sixteen patients with acute BAI were reviewed. The mean Injury Severity Score was 36.9 (SD, 17.0), with a median intensive care unit LOS of 7.5 days (interquartile range, 1-17 days) and total LOS of 24.5 days (interquartile range, 7-41 days). Mean follow-up was 10.7 months (range, 3-30 months). Technical success was achieved in all patients, no graft-related complications have been detected during follow-up, and no patient developed postoperative paraplegia. One postoperative mortality occurred secondary to severe comorbid injury. A single patient with a preoperative traumatic carotid dissection demonstrated a postoperative stroke, and one patient required thoracentesis for a pleural effusion. CONCLUSION: Repair of BAI with EVSG can be performed safely in patients with BAI. Mortality, morbidity, and especially paraplegia are reduced. Further long-term studies are required to support the routine use of EVSG technology for BAI.
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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) | 0.000 | 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".