Testing Flow Diverters in Giant Fusiform Aneurysms: A New Experimental Model Can Show Leaks Responsible for Failures
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: FDs are the latest, most promising tool to treat giant and complex aneurysms. Currently available experimental aneurysm models do not reproduce the potential clinical difficulties of treating these lesions with FDs. MATERIALS AND METHODS: Six large or giant canine fusiform aneurysms were created on the distal carotid arteries of 4 animals. Four of the aneurysms had multiple arterial branches originating from the aneurysm; 2 other aneurysms had all branches clipped at the time of aneurysm construction. Aneurysms were treated with multiple telescoping prototype flow-diverting stents (total of 15 FDs). Angiography was carried out before and immediately after implantation, at 2 weeks, and immediately before sacrifice at 12 weeks. Macroscopic photography of specimens was performed, followed by biopsies of selected regions of the tissue formed on the surface of FDs. RESULTS: Technical or device-related difficulties occurred in 2 of 6 aneurysm treatments. Fusiform aneurysms with branches intact remained widely patent (mean angiographic score, 3), whereas aneurysms with clipped branches had only small residua (mean angiographic score, 1) at 12 weeks. The presence of very small defects in neointima formation on the surface of FDs, or leaks, was sufficient for residual filling of the aneurysms, which served as reservoirs to feed branches. CONCLUSIONS: Experimental canine fusiform carotid aneurysms may reproduce many of the difficulties associated with the treatment of giant aneurysms and could be appropriate for preclinical testing of FD stents.
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 | 0.001 | 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 | 0.000 | 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".