The hereditary basis of bicuspid aortic valve disease: a role for screening?
Bibliographic record
Abstract
Lara Gharibeh, Mona Nemer Molecular Genetics and Cardiac Regeneration Laboratory, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, Canada Abstract: Over the past years, human and molecular genetic studies have provided new understanding of valve development and the molecular pathogenesis of bicuspid aortic valve (BAV) disease. BAV is an autosomal dominant disease with incomplete penetrance and is found to affect 1%–2% of the population. It can occur in isolation or coexists with other congenital heart diseases such as ventricular septal defect and tetralogy of fallot. BAV is a risk factor for premature cardiovascular disease and can lead to severe complications affecting the aorta and the valves. To date, NOTCH1 and GATA5 are the only genes linked to human BAV, and the genetic basis for most BAVs remains unidentified. Large-scale screening as well as whole exome sequencing studies hold promise for uncovering BAV-causing genes. Similarly, molecular analysis of valve development in animal models is needed for better insight of normal and pathologic valve formation. Together, these approaches will undoubtedly accelerate discovery of disease-causing genes opening the way for early diagnosis of BAV and prevention of valve degeneration and cardiovascular complications. Keywords: congenital heart disease, valvulogenesis, genetic screening
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".