A modeling framework for computational simulations of thoracic endovascular aortic repair
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thoracic endovascular aortic repair (TEVAR) is a minimally invasive treatment for thoracic aortic conditions including aneurysms and is associated with a number of postoperative stent graft related complications. Computational simulations of TEVAR have the potential to predict surgical outcomes and complications preoperatively. When using simulations for stent graft design and prediction of complications in a population, it is difficult to generalize patient-specific TEVAR computational models due to patient variability. This study proposes a novel modeling framework for creating realistic population-based computational models of TEVAR focused on aneurysms that allow for developing various clinically relevant geometric configurations and scenarios that are not easily attainable with limited patient data. The framework includes a methodology for developing population-based thoracic aortic geometries and defining age-dependent aortic tissue material models, as well as detailed steps and boundary conditions for finite element modeling of stent graft deployment during TEVAR. The simulation framework is illustrated for predicting the formation of a bird-beak configuration, a wedge-shaped gap at the proximal end of the deployed stent graft in TEVAR that leads to incomplete seal. A baseline TEVAR simulation model was developed along with three simulations in which the value of aortic curvature, aortic arch angle, or aortic tissue properties varied from the baseline model. Analyzing the length and angle of the bird-beak configuration in each case shows that the bird-beak size is sensitive to different values of the aortic geometry highlighting the importance of using realistic parameter values.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 it