Promoting Suitable Hemodynamic Conditions for Thrombus Formation in Abdominal Aortic Aneurysms With Multilayer Stents
Bibliographic record
Abstract
Multilayer stents or flow modulators are novel devices used for endovascular repair of abdominal aortic aneurysms (AAAs) in patients with unsuitable or complex endovascular geometries. The use of this device is still controversial due to insufficient patient follow-up data and the lack of CFD studies addressing the problem. In this work, we present a methodology to design the flow resistance characteristics needed from multilayer stents to induce suitable hemodynamic conditions inside the aneurysm sac. A hypothetical AAA geometry with and without the device is employed to study the impact on hemodynamic factors of interest such as: the shear stress and pressure distribution on the aneurysm wall, their maximum and average values, and the flow fields inside the aneurysm sac. To model the stent, the porosity and the flow resistance values are used to scale the mass and the Naviers’Stokes equations, including the contribution of the flow resistance on the momentum source term. Through a sensitivity study, the magnitude of the porosity is systematically varied to find the resistance profile that promotes suitable hemodynamic conditions inside the AAA, consistent with the goal of promoting intraluminal thrombus formation. Our results have important implications for designing the layer structures of the multilayer stent to reduce the risks 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".