Numerical simulation of blood flow in intracranial aneurysms treated by endovascular Woven EndoBridge technique
Bibliographic record
Abstract
Over the past few decades, different therapeutic methods for the treatment of intracranial aneurysms have been developed. During recent years, novel standalone intrasaccular Woven EndoBridge (WEB) technique has paved the way for efficient therapy and reduced some deficiencies in prior procedures. Blood hemodynamics plays a crucial role in occurrence and perpetuating of aneurysm; therefore, understanding of relevant parameters can lead to a better treatment and evolution of design. Objectively, this paper has established the first mathematical framework to explore hemodynamic parameters for WEB-treated saccular aneurysms by employing Computational Fluid Dynamics (CFD). Two ideal models of artery — one is suffered by a bifurcation aneurysm at Basilar Artery (BA) and another Posterior Cerebral Artery (PCA) aneurysm — are selected. Simulations are performed for an untreated and three WEB-treated aneurysms by Dual Layer (DL), Single Layer (SL) and Single Layer Sphere (SLS) WEBs. Results demonstrate that, generally, the WEB reduces flow intrusion and circulation inside the aneurysm sac, which leads to lowering WSS; however, the infiltrated flow to the WEB causes slight increase in intrasaccular pressure. Moreover, the numerical results show that the WEB DL reduces velocity and WSS, and elevates pressure inside the sac more than the WEBs SL and SLS. Among the explored WEB models (DL, SL and SLS), by assuming thorough binding at the aneurysm neck, the WEB DL demonstrates much efficient performance in flow diversion from the aneurysm, while despite the different structure of WEBs SL and SLS, they perform similarly.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".