Implant Degradation and Poor Healing After Endovascular Repair of Abdominal Aortic Aneurysms: An Analysis of Explanted Stent-Grafts
Bibliographic record
Abstract
PURPOSE: To study explanted stent-grafts to achieve a better understanding of the mechanisms of failure after endovascular treatment of abdominal aortic aneurysms (AAA). METHODS: Twelve stent-grafts were harvested at autopsy (n=3) or during surgical conversion (n=9). Device alterations were investigated by macroscopic examination, radiography, and surface analysis techniques. Healing around the implants was studied via histology and immunohistochemistry, with particular attention to the stent-graft/tissue interface. RESULTS: Degradation was more important with Vanguard stent-grafts (off the market) than with AneuRx and Talent stent-grafts, but rupture of nitinol wires and poor surface finish in Talent stent-grafts raise concern about their corrosion resistance and long-term stability. Poor healing was observed around stent-grafts even after several years of implantation, with absence of vascular smooth muscle cells, fibroblasts, and collagen formation. In addition to the well-known foreign body reaction around the graft, numerous polymorphonuclear cells characteristic of the first step of healing were present in tissues around stent-grafts retrieved at surgical conversion. Factors explaining the lack of tissue organization around stent-grafts are discussed. CONCLUSION: The long-term stability of implants remains a concern and requires more transparency from manufacturers regarding the surface properties of their devices. Lack of neointima formation impairs biological fixation of the implant to the vessel wall, leading to possible endoleaks and migration. New-generation stent-grafts promoting biological fixation should be developed to improve clinical outcomes of this minimally invasive treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".