Testing Stenting and Flow Diversion Using a Surgical Elastase-Induced Complex Fusiform Aneurysm Model
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Rabbit elastase-induced saccular aneurysms have been commonly used for preclinical testing of endovascular devices, including flow diverters. However, all tested devices have been shown to be capable of aneurysm occlusion with this model. We aimed to create a more challenging model to test and discriminate among neurovascular devices of varying efficacies. MATERIALS AND METHODS: With a surgical approach that included elastase infusion and balloon dilation, we attempted the creation of complex fusiform aneurysms in 16 rabbits, with standard saccular carotid aneurysms created in 15 other animals. Aneurysms were randomly allocated to one of the following treatments: flow diversion (n = 8), high-porosity stent (n = 6), double high-porosity stent (n = 5), and control (n = 6). Angiographic assessment and pathologic analyses were performed at 3 months. RESULTS: Creation of complex fusiform and standard saccular aneurysms was successful in 12/16 and 13/15 attempts, respectively. All saccular (n = 4) or complex fusiform (n = 4) aneurysms treated with flow diverters were successfully occluded. Three of 3 saccular compared with 0/2 complex fusiform aneurysms were occluded by double high-porosity stents. One of 3 saccular and 0/3 complex fusiform aneurysms were occluded by a single high-porosity stent. Both aneurysm types shared the same pathologic findings when untreated: The aneurysm wall lacked an elastic layer and smooth muscle cells, while the lumen was lined with neointima of varying thickness. Neointimal coverage of the devices was complete when aneurysms were occluded, while leaks were always associated with aneurysm remnants. CONCLUSIONS: Challenging fusiform aneurysms can be created in rabbits by using a surgical modification of the elastase method.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".