The Role of Medical Therapy in Management of Bicuspid Aortic Valve–Associated Aortopathy in Children
Bibliographic record
Abstract
Background Patients with bicuspid aortic valve (BAV) are often treated with medication to slow the rate of aortic dilatation, without established efficacy. Methods We conducted a retrospective, multicentre study of 558 children (83 treated and 475 not treated) with BAV and ascending aorta (AscAo) dilatation. The median follow-up was 3.6 years for treated patients and 5.6 years for not treated patients. Longitudinal mixed models assessed the rate of AscAo and sinus of Valsalva (SoV) dilatation expressed as a change in Z score units per year for patients treated and not treated with a β-blocker or an afterload-reducing agent. Secondary outcomes included time to significant AscAo dilatation ( Z score ≥6) and proportions of patients achieving Z score stabilization (dilatation rate <0.1 Z /y). Results Compared with untreated patients, those treated had a small reduction of AscAo and SoV dilatation rates with an absolute treatment difference of −0.032 Z /y (95% confidence interval [CI]: −0.086 to 0.022) and −0.021 Z /y (95% CI: −0.078 to 0.035), respectively. Patients treated had a small reduction of the time to significant dilatation of AscAo (hazard ratio: 0.83; 95% CI: 0.43-1.61). Patients treated were more likely to achieve Z score stabilization with an increase in the proportion of patients by 4.5% for AscAo (95% CI: −11.3% to 20.2%) and 7% for SoV (95% CI: −9.7% to 22.5%). Overall, the probability of a null effect was high, as the 95% CI for all outcomes between the groups overlapped. Conclusion Pharmacologic treatment was not associated with a meaningful reduction of AscAo and SoV dilatation rates in children with BAV.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".