Computational Fluid Dynamics Modeling of Redundant Stent-Graft Configurations in Endovascular Aneurysm Repair
Bibliographic record
Abstract
An aneurysm is a bulge or localized dilation of an artery that can result in rupture, rapid blood loss, and death. Endovascular aneurysm repair (EVAR) is a minimally-invasive surgical technique that involves delivery of a stent-graft from within the blood vessels. The metallic stents anchor and support the graft (fabric tube), through which blood flow is contained and directed. This relieves the pressure on the weakened aneurysm wall. When the stent-graft is too long for a given patient, the redundant (extra) length adopts a convex configuration in the aneurysm. Based on clinical experience, we hypothesize that redundant stent-graft configurations increase the downward force acting on the device thereby increasing the risk of device dislodgement and failure. This work numerically studies both steady-state and physiologic pulsatile blood flow in redundant stent-graft configurations. Computational fluid dynamics simulations predicted peak downward displacement force for the zero-, moderate- and severe-redundancy configurations of 7.49, 7.65 and 8.04 N, respectively for steady-state flow; and 7.55, 7.70 and 8.31 N, respectively for physiologic pulsatile flow. These results suggest that redundant stent-graft configurations in EVAR do increase the downward force acting on the device, but the clinical consequence depends significantly on device-specific resistance to dislodgement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".