Optimization of porous stents for endovascular repair of abdominal aortic aneurysms
Bibliographic record
Abstract
This study presents a simulation-based methodology to design porous stents to induce suitable hemodynamic environments inside abdominal aortic aneurysm (AAA) sacs. In the proposed methodology, an optimization algorithm iteratively modifies the porosity distribution of the stent and executes a computational fluid dynamics (CFD) simulation to determine the effect of these changes on the hemodynamic conditions inside the aneurysm sac. The optimization iterations proceed until relevant hemodynamic parameters are within ranges prescribed a priori by the user as desirable to control the progression of the AAA. The resulting porosity distribution uniquely describes the porous stent design that can control the hemodynamic environment (eg, shear stress at the aneurysm wall, pressure distribution, residence time), reducing AAA rupture risks and improving treatment efficacy. To demonstrate its potential, the proposed methodology is applied to idealized AAA geometry under steady-state flow conditions, though it may be easily applied to more complex AAA geometries under transient, pulsatile flow conditions. The proposed methodology has the potential to enable the design of a new generation of porous stents tailored to patient-specific geometries and flow conditions, to improve patient outcomes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".