Editorial: Bicuspid aortic valve: from pathophysiological mechanisms, imaging diagnosis to clinical treatment methods
Bibliographic record
Abstract
Editorial on the Research Topic Bicuspid aortic valve: from pathophysiological mechanisms, imaging diagnosis to clinical treatment methods This Research Topic, entitled "Bicuspid Aortic Valve: From pathophysiological mechanisms, imaging diagnosis to clinical treatment methods", is created to set a forum for researches that tackle the difference or uniqueness of the BAV entity, from genetic, cellular and pathophysiological mechanisms of BAV and the subsequent bicuspid aortic stenosis, clinical imaging for bicuspid aortic stenosis to unveil its function and anatomy, treatment innovations and strategies tailored for bicuspid aortic stenosis, to clinical outcomes.Herein, we introduce the 13 articles collected in this Research Topic (Table 1). Genomic issues in congenital BAVThe genetics behind BAV are acknowledged to be different from a normal tricuspid aortic valve (TAV), as patients with BAV may develop aortic malformations, valvular dysfunctions, or symptoms at a younger age.Several genomic mutations have been found to be associated with BAV, such as mutations in NOTCH1, ROBO4, etc. (1).In this research topic, Jacob Gutierrez et al. identified that patients with Turner syndrome (TS), a rare cytogenetic disorder presenting a 60-fold increased risk of BAV compared to the general population, have differentially methylated regions (DMRs) encompassing MYRF and enrichment for genomic targets, including genes in NOTCH1 and the downstream gene MYH11 in those with concomitant BAV.These DMRs in TS appeared to contribute to both BAV development and BAV-associated aortopathy, adding evidence in the genomic etiology of congenital 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.026 | 0.021 |
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".