A New Canine Carotid Artery Bifurcation Aneurysm Model for the Evaluation of Neurovascular Devices
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stents are increasingly used for coiling of difficult aneurysms, to reduce the risk of recurrences, or to modify blood flow. Currently available bifurcation aneurysm models are ill-suited to assess stent performance before clinical use. We designed a new wide-neck canine T-type bifurcation aneurysm model. Its potential value as a training tool as well as in the evaluation of new techniques or embolic agents was assessed. Our first task was to verify that recurrences occurred after satisfactory coiling. A second aim of this preliminary work was to assess if the new model could recreate the technical challenges involved in bifurcation aneurysms. MATERIALS AND METHODS: We introduce a new canine wide-neck bifurcation aneurysm model, created by using a vein pouch at the apex of an end-to-side anastomosis of the carotid arteries, with flow reversal in the proximal RCA by ligation of the innominate artery. Three aneurysms were treated with coil embolization, 10 were treated with stents (7 self-expandable, 3 balloon-expandable), and 3 were left untreated. Aneurysms were followed by duplex ultrasonography and angiography, and studied with macroscopic photography after euthanasia 11.8 +/- 3.9 months after surgery. RESULTS: All aneurysms remained patent at 9.0 +/- 3.6 months' follow-up. Coiling led to recurrences by 3 months in all 3 cases. Stent placement was technically difficult in all cases and did not lead to aneurysm thrombosis or neointimal closure of the aneurysm neck at 3 months. CONCLUSIONS: This model may be suitable for studying the effects of endovascular treatment on aneurysm and branch occlusion rates, for preclinical testing of stents and other intravascular devices, and for training students of endovascular 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".