Applications of a Specialty Bicuspid Aortic Valve Program: Clinical Continuity and Translational Collaboration
Bibliographic record
Abstract
Bicuspid aortic valve (BAV) is a common congenital heart diagnosis and is associated with aortopathy. Current guidelines for aortic resection have been validated but are based on aortic diameter, which is insufficient to predict acute aortic events. Clinical and translational collaboration is necessary to identify biomarkers that can individualize the timing of prophylactic surgery for BAV aortopathy. We describe our multidisciplinary BAV program, including research protocols aimed at biomarker discovery and results from our longitudinal clinical registry. From 2012-2018, 887 patients enrolled in our clinical BAV registry with the option to undergo four dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR) and donate serum plasma or tissue samples. Of 887 patients, 388 (44%) had an elective BAV-related procedure after initial presentation, while 499 (56%) continued with medical management. Of medical patients, 44 (9%) had elective surgery after 2.3 ± 1.4 years. Surgery patients' biobank donations include 198 (46%) aorta, 374 (86%) aortic valve, and 314 (73%) plasma samples. The 4D flow CMR was completed for 215 (50%) surgery patients and 243 (49%) medical patients. Patients with BAV aortopathy can be safely followed by a multidisciplinary team to detect indications for surgery. Paired tissue and hemodynamic analysis holds opportunity for biomarker development in BAV aortopathy.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".