Evolution of Precision Medicine and Surgical Strategies for Bicuspid Aortic Valve-Associated Aortopathy
Bibliographic record
Abstract
Bicuspid aortic valve (BAV) is a common congenital cardiac malformation affecting 1-2% of people. BAV results from fusion of two adjacent aortic valve cusps, and is associated with dilatation of the aorta, known as bicuspid valve associated aortopathy. Bicuspid valve aortopathy is progressive and associated with catastrophic clinical events, such as aortic dissection and rupture. Therefore, frequent monitoring and early intervention with prophylactic surgical resection of the proximal aorta is often recommended. However, the specific pattern of aortopathy is highly variable among patients, with different segments of the ascending aorta being affected. Individual patient risks are sometimes difficult to predict. Resection strategies are informed by current surgical guidelines which are primarily based on aortic size and growth criteria. These criteria may not optimally reflect the risk of important aortic events. To address these issues in the care of patients with bicuspid valve aortopathy, our translational research group has focused on validating use of novel imaging techniques to establish non-invasive hemodynamic biomarkers for risk-stratifying BAV patients. In this article, we review recent efforts, successes, and ongoing challenges in the development of more precise and individualized surgical approaches for patients with bicuspid aortic valves and associated aortic disease.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".