Management of Blunt Thoracic Aortic Injuries: Endovascular Stents versus Open Repair
Bibliographic record
Abstract
BACKGROUND: Endovascular stent graft (EV) technology has been successfully adapted to the repair of blunt traumatic aortic injuries. The purpose of this study was to compare the outcomes of patients treated with EV repair and open repair after blunt thoracic aortic trauma. METHODS: A review of a tertiary trauma center's prospective trauma registry identified all patients who suffered a blunt traumatic thoracic aortic injury over an 11-year period (1991-2002). Operative interventions and outcomes were then compared. RESULTS: Over an 11-year period, 18 patients underwent repair of a blunt thoracic aortic injury (EV, 6; open, 12). There were no significant differences in demographics, injury, or crash statistics between groups. The open group had a 17% early mortality rate (n = 2), a paraplegia rate of 16% (n = 2), and an 8.3% incidence of recurrent laryngeal nerve injury (n = 1). This is in contrast to a 0% rate of mortality, paraplegia, and recurrent laryngeal nerve injury in the EV group. A definite trend toward decreased morbidity, mortality, intensive care unit length of stay, and number of ventilator-dependent days was seen with EV repair. CONCLUSION: We observed a clear trend toward improved outcomes after EV repair of thoracic aortic injuries compared with standard open repair. EV repair is emerging as the preferred method of repairing blunt thoracic aortic injuries in trauma patients with multiple injuries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".