Valve disease and aortopathy associations of bicuspid aortic valve phenotypes differ between men and women
Bibliographic record
Abstract
OBJECTIVE: Determine whether associations between bicuspid aortic valve (BAV) phenotypes, valve disease and aortopathy differ between sexes. METHODS: 1045 patients with BAV (76.0% men, n=794) from two surgical centres were included in this cross-sectional study. Valve phenotype was classified intraoperatively as right-left (RL), right-non-coronary (RN), left-non-coronary (LN) or 2-sinus BAV. Echocardiography was used to determine type and degree of valve disease, and aortic dimensions. Aortic dilatation was defined as diameter ≥4.5 cm. RESULTS: RL was the most common phenotype (73.6%), followed by RN (16.2%), 2-sinus BAV (9.2%) and LN (1.1%), with no difference in phenotype distribution between men and women (p=0.634). Aortic valve insufficiency (AI) prevalence differed significantly with valve phenotype in men (p=0.047), with RL and LN having the highest prevalence (34.1% and 44.0%, respectively). In women, RN had a higher proportion of AI than RL (21.3% vs 7.3%, p=0.017). Men with RL had larger root dimensions, in particular at the sinus (mean difference 0.24 cm compared with RN, p=0.002). Men with 2-sinus BAV had the highest prevalence of root phenotype dilatation (7.0%, other phenotypes ≤2.3%, p=0.031), whereas women with 2-sinus BAV did not have root dilatation and smaller sinus dimensions (mean difference: 0.35 cm compared with RL, p=0.021). Aortic root segments were larger in men with AI compared with aortic stenosis (sinus mean difference: 0.40 cm, p<0.001). The difference was even larger in women (mean difference: 0.78 cm, p<0.001), and women with AI also had larger tubular segments (mean difference: 0.61 cm, p=0.001). CONCLUSIONS: There are significant sex differences in clinical associations of BAV phenotypes, which should be considered in further studies on the role of phenotypes in individualised patient management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".