P.112 Mechanical properties of fusiform aneurysms in a rabbit model
Bibliographic record
Abstract
Background: Animal models of human cerebral aneurysms have been a vital part of the development of endovascular treatments for decades. Rabbit models have been successfully used to simulate the morphology and hemodynamics of human intracranial aneurysms. However, the lack of mechanical testing of human intracranial aneurysm tissue limits our understanding of the mechanisms of aneurysm rupture. The goal of this project is to develop techniques for the mechanical testing of fusiform aneurysms in a rabbit model. Methods: Fusiform aneurysms were created using the right carotid artery using an elastase-based method. Thirty fusiform aneurysms and healthy rabbit carotid artery samples were then collected from our lab and tested with a uniaxial and biaxial loading system. Rectangular strips of aneurysm and healthy tissue were obtained in the axial and circumferential direction with a micro-cutting instrument. The test samples were gripped by a custom-designed micro-clamp and placed in a bath of phosphate-buffered saline at 37˚C temperature. Results: Maximum stress of healthy and aneurysm arteries are 50 Kpa and 0.6 Kpa Conclusions: The strength of healthy tissue was significantly higher than tissue from the fusiform aneurysm. These techniques will provide us with strategies for the eventual testing of human intracranial tissue and may help us to understand mechanisms of aneurysm rupture.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".