Abstract 13071: Computational Simulation of Virtual Ross Operation
Bibliographic record
Abstract
Background: Unlike other bioprosthetic and mechanical valve replacement strategies, the Ross procedure has been shown to normalize patient survival comparable to an age-matched general population. However, one of the main limitations of the Ross procedure is the need for reoperation due to autograft dilatation or aortic insufficiency. The objective of this study was to develop a patient-specific virtual Ross operation with full root-replacement technique using computational simulation to investigate pulmonary autograft biomechanics early after surgery. Methods: A patient scheduled for Ross procedure underwent preoperative cine-cardiac magnetic resonance imaging (MRI). MRI lumen geometry was used to create surface contour meshes of aortic sinuses and pulmonary autograft. Using reverse engineering approach, zero-pressure geometries of aortic root and pulmonary autograft were created; unloaded wall thickness and autograft material properties were included. Using LS-DYNA software, simulations were performed of surgical suturing aortic root replacement with a pulmonary autograft. Sutured autograft and remaining aorta were then loaded to systemic pressure. Results: Average von Mises stress increased from 62kPa preoperatively in the native pulmonary root in situ at pulmonary pressure to 373.8kPa in the autograft as root replacement at systemic pressure, while postoperative von Mises stress in remaining aortic wall was 107.1kPa (fig 1). Conclusions: We performed patient-specific virtual Ross operation using computational simulation. Increased stress in pulmonary autograft indicated areas prone to possible structural failure. Reinforcement in these regions to support autograft wall may reduce future reoperation. Such patient-specific simulations to analyze stress concentrations may be an effective tool to optimize surgical techniques and determine patient-specific risk of aneurysm formation.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".