Short-term results in canines of novel stent-graft design for chimney technique in TEVAR
Bibliographic record
Abstract
INTRODUCTION: Parallel stent-stent grafting is a major endovascular technique used to preserve the supra-aortic branches during thoracic endovascular aortic repair (TEVAR) of aortic pathologies involving the aortic arch. The short- and mid-term results of this technique are satisfactory; however, endoleak remains a major concern. Thus, here we designed a new chimney stent-graft to decrease the endoleak rate. AIM: To testify the feasibility and safety of the new chimney stent-graft system in a canine model. MATERIAL AND METHODS: Six Labrador retrievers were used. Pre-operative data were collected and all operations were performed under general anesthesia. The main and chimney stent-grafts were implanted through the abdominal aorta and left subclavian artery approaches, respectively. Completion digital subtraction angiography (DSA) was performed to confirm the immediate outcomes. All dogs were fed separately for 6 months and sacrificed after aortic angiography. The thoracic aorta and the main and chimney stent-grafts were harvested for histopathologic examination. RESULTS: No complications were found in follow-up DSA. All branch arteries were patent. Inflammatory responses were observed around the stent-grafts in 3 experimental animals, and slight hyperplasia was observed in the surrounding tissues compared with the normal vessels. There was no mural thrombus in the stent, endothelial cells were noted on the inner surface of the stent, and thrombus was formed in the outer skirt and gutter area. The histopathologic examinations revealed similar results to those of gross necropsy observations. CONCLUSIONS: This study demonstrated the feasibility and safety of the Longuette stent-graft and the first to report a revised stent-graft specific for chimney technique.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".