A High-Fidelity Finite Element Model for Simulating and Optimizing the Life Cycle of a Breakthrough Aneurysm Occluding Device: Crimping and Deployment Mechanisms
Bibliographic record
Abstract
Abstract Advanced treatment options of cerebral aneurysms (CAs), in particular percutaneous treatment, are gaining fast attention by researchers and practitioners. The eCLIPs implant (product of Evasc Neurovascular Enterprises, Vancouver, Canada) has recently revolutionized the percutaneous treatment of CAs by offering innovative solutions to the challenges pertinent to other neurovascular devices, i.e. excessive vessel injury caused by device and artery interaction and blocking the daughter vessels in bifurcation cases. However, in a subset of CAs at the bifurcation location with fusiform pathology, eCLIPs fails to provide sufficient neck bridging, where a gap exists between the device structure and the aneurysm/artery wall upon device deployment. We have proposed an innovative solution for this problem by developing a new design for the eCLIPs (VR-e) by making the length of device ribs variable to cover such an inflow gap. We have developed a new computational framework to optimize the new product development process and evaluate the device behavior during crimping into a catheter and expansion at the aneurysm neck, which is not possible by testing a new device for the endovascular application experimentally. In spite of the longer rib span in the VR-e over eCLIPs, the device structure has not experienced plastic deformation during the crimping process. The VR-e device fully expanded and covered the inflow gap when deployed at the neck.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".