Long-Term Risk Factors for Dilatation of the Proximal Aorta in a Large Cohort of Children With Bicuspid Aortic Valve
Bibliographic record
Abstract
Background: Patients with bicuspid aortic valve (BAV) have a higher risk of developing aortic valve dysfunction and progressive proximal aorta dilatation, which can lead to aortic dissection. To this day, identification of children at risk of developing severe aortic dilatation during their pediatric follow-up is still challenging because most studies were restricted to adult subjects. The overarching goal of this study was to identify risk factors of aortic dilatation in children with BAV. Methods: We extracted clinical and echocardiographic data of all BAV subjects aged 0 to 20 years followed at Centre Hospitalier Universitaire Sainte-Justine between 1999 and 2016. We excluded subjects with concomitant heart defects and conditions affecting proximal aorta dimensions. Proximal aorta diameters (expressed as Z scores) were modeled in relation to age and potential predictive variables in a linear mixed model. The primary outcome was the rate of dilatation. Results: We included 761 subjects (3134 echocardiograms) in final analyses. The mean ascending aorta Z score progression rate for BAV patient with a normally functioning aortic valve was estimated at 0.05 Z score unit per year. The strongest predictors of an increased dilatation rate were severe aortic stenosis, moderate and severe aortic regurgitation, and uncorrected coarctation of the aorta. Aortic valve leaflet fusion pattern and sex were not associated with progression rate. Conclusions: Children with a normally functioning BAV exhibited a very slow proximal aorta dilatation rate. Ascending aorta dilatation rate was significantly increased in patients with more than mild aortic valve dysfunction but was independent from BAV leaflet fusion type.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".