Predicting Rotation in Fenestrated Endovascular Aneurysm Repair Using Finite Element Analysis
Bibliographic record
Abstract
Fenestrated endovascular aneurysm repair (FEVAR) is a minimally invasive method of abdominal aortic aneurysm (AAA) repair utilized in patients with complex vessel anatomies. Stent grafts (SG) used in this process contain fenestrations within the device that need to be aligned with the visceral arteries upon successful SG deployment. Proper alignment is crucial to maintain blood flow to these arteries and avoid surgical complications. During fenestrated SG deployment, rotation of the SG can occur during the unsheathing process. This leads to misalignment of the vessels, and the fenestrations and is associated with poor clinical outcomes. The aim of this study was to develop a computational model of the FEVAR process to predict SG rotation. Six patient-specific cases are presented and compared with surgical case data. Realistic material properties, frictional effects, deployment methods, and boundary conditions are included in the model. A mean simulation error of 2 deg (range 1-4 deg) was observed. This model was then used to conduct a parameter study of frictional properties to see if rotation could be minimized. This study showed that increasing or decreasing the coefficients of friction (COF) between the sheath and the vessel walls would decrease the amount of rotation observed. Our model accurately predicts the amount of SG rotation observed during FEVAR and can be used as a preoperative planning tool within the surgical workflow.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".