Development of a New Generation of Neurovascular Devices for the Treatment of Cerebral Bifurcation Aneurysms with the Fusiform Opathology: A Computational Approach
Bibliographic record
Abstract
Abstract Cerebral aneurysm (CA) is an abnormal dilation of the cerebral arterial wall, which accounts for more than half a million deaths each year worldwide. Flow diverters (FDs) represent one method recently developed in treating CAs. Typically, they do not need coiling (releasing micro-coils within the aneurysm) and act purely to prevent substantial blood inflow into the aneurysm. In collaboration with Evasc Neurovascular Enterprises (Vancouver, Canada), whose area of expertise is developing novel CA therapies, we have developed a novel FD for the treatment of bifurcation CAs with fusiform-like properties involving the confluence of the main and daughter branches. To the best of authors’ knowledge, currently there is no device for an effective treatment of such complex aneurysms. Through a stepwise design modification process and utilizing CFD modeling, we have developed a new design for the Evasc FD (eCLIPs) with improved hemodynamics, which is characterized by more than 30% reduction in the aneurysm inflow and wall shear stress (WSS) for the new implant design over eCLIPs for this subset of aneurysms. The new device design, modified-design eCLIPs (MD-eCLIPs), can represent the only device available for the treatment of such CAs with fusiform pathology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".