Prediction of bird-beak configuration in thoracic endovascular aortic repair preoperatively using patient-specific finite element simulations
Bibliographic record
Abstract
Objectives: Formation of bird-beak configuration in thoracic endovascular aortic repair (TEVAR) has been shown to be correlated with the risk of complications such as type Ia endoleaks, stent graft migration, and collapse. The aim of this study was to use patient-specific computational simulations of TEVAR to predict the formation of bird-beak configuration preoperatively. Methods: Patient-specific TEVAR computational simulations are developed using a retrospective cohort of patients treated for thoracic aortic aneurysm. The preoperative computed tomography images were segmented to develop three-dimensional geometry of the thoracic aorta. These geometries were used in finite element simulations of stent graft deployment during TEVAR. Simulated results were compared against the postoperative computed tomography images to assess the accuracy of simulations in predicting the proximal position of a deployed stent graft and presence of bird-beak. In cases with a bird-beak configuration, the length and angle of the bird-beak were measured and compared between the simulated and postoperative results. Results: Twelve TEVAR patient cases were simulated. Computational simulations were able to accurately predict whether the proximal stent graft was fully apposed, proximal bare stents were protruded, or bird-beak configuration was present. In three cases with bird-beak configuration, simulations predicted the length and angle of the bird-beak with less than 10% and 24% error, respectively. Other factors such as a small aortic arch angle, small oversizing value, and landing zones close to the arch apex may have played a role in formation of bird-beak in these patients. Conclusions: Computational simulations of TEVAR accurately predicted the proximal position of a deployed stent graft and the presence of bird-beak preoperatively. The computational models were able to predict the length and angle of bird-beak configurations with good accuracy. These simulations can provide insight into the surgical planning process with the goal of minimizing bird-beak occurrence.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".