The Hemodynamics of Aneurysms Treated with Flow-Diverting Stents Considering both Stent and Aneurysm/Artery Geometries
Bibliographic record
Abstract
Flow diverting stents are deployed to reduce the blood flow into the aneurysm, which would thereby induce thrombosis in the aneurysm sac; the stents prevent its rupture. The present study aimed to examine and quantify the impacts of different flow stents on idealized configurations of the cerebral artery. In our study, we considered a spherical sidewall aneurysm located on curved and tortuous idealized artery vessels and three stents with different porosities (70, 80 and 90%) for deployment. Using computational fluid dynamics, the local hemodynamics in the presence and absence of the stents were simulated, respectively, under the assumption that the blood flow was unsteady and non-Newtonian. The hemodynamic parameters, such as the intra-aneurysmal flow, velocity field and wall shear stress and its related indices, were examined and compared among the 12 cases simulated. The results illustrated that with the stent deployment, the intra-aneurysmal flow and the wall shear stress and its related indices were considerably modified depending on both stent and aneurysm/artery geometries, and that the intra-aneurysmal relative residence time increased rapidly with decreasing stent porosity in all the vessel configurations. These results also inform the rationale for selecting stents for treating aneurysms of different configurations.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".