Implications of Bicuspid Aortic Valve Disease and Aortic Stenosis/Insufficiency as Risk Factors for Thoracic Aortic Aneurysm
Bibliographic record
Abstract
Bicuspid Aortic Valves (BAV) are associated with an increased incidence of thoracic aortic aneurysms (TAA). TAA are a common aortic pathology characterized by enlargement of the aortic root and/or ascending aorta, and may become life threatening when left untreated. Typically occurring as the sole pathology in a patient, TAA are largely asymptomatic. However, in some instances, they are accompanied by aortic valve (AV) diseases: either congenital BAV or acquired in the form of Aortic Insufficiency (AI) or aortic stenosis (AS). When TAA are associated with aortic valve disease, determining an accurate and predictable prognosis becomes especially challenging. Patients with AV disease and concomitant TAA lack a widely accepted diagnostic approach, one that integrates our knowledge on aortic valve pathophysiology and encompasses multi-modality imaging approaches. This review summarizes the most recent scientific knowledge regarding the association between AV diseases (BAV, AI, AS) and ascending aortopathies (dilatation, aneurysm, and dissection). We aimed to pinpoint the gaps in monitoring practices and prediction of disease progression in TAA patients with concomitant AV disease. We propose that a morphological and functional analysis of the AV with multi-modality imaging should be included in aortic surveillance programs. This strategy would allow for improved risk stratification of these patients, and possibly new AV phenotypic-specific guidelines with more vigilant surveillance and earlier prophylactic surgery to improve patient outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".