Midterm Results Following Endovascular Repair of Blunt Thoracic Aortic Injuries
Bibliographic record
Abstract
OBJECTIVE: Previous studies have focused on early outcomes of thoracic endovascular repair (TEVAR) of blunt thoracic aortic injuries (BTAIs). Late results remain ill-defined. The purpose of this study is to review the midterm results of our experience with endovascular repair of BTAIs. METHODS: A retrospective analysis was performed reviewing all endovascular repairs of BTAIs from 2002 to present. Preoperative, operative, and postoperative variables were recorded. Clinical end points included aortic-related mortality, stroke and paraplegia, hospital length of stay, procedure-related complications, endoleaks, and reinterventions. Computed tomography data sets were postprocessed for assessing integrity of stent grafts and late complications. RESULTS: A total of 24 cases of BTAIs treated with TEVAR were identified. Thoracic endovascular repair was successful in treating BTAIs in all patients and there were no instances of procedure-related death, stroke, or paraplegia. One access complication occurred, requiring an iliofemoral bypass. Actuarial survival estimates and freedom from reintervention at 5 years were 88.7% and 95.8%, respectively. No late endoleaks, stent fractures, or device migration were identified. One patient required a secondary intervention 1 year following the initial repair to treat a pseudocoarctation syndrome caused by a diaphragm at the distal half of the stented aorta. This was treated successfully with repeated endografting. CONCLUSIONS: Thoracic endovascular repair for BTAIs can be performed safely with low periprocedural mortality and morbidity. Midterm follow-up data presented in this report further support the therapeutic role of endoluminal approach for treating BTAIs in anatomically suitable patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".