Determinants of Aortic Stenosis Progression in Bicuspid and Tricuspid Aortic Valves
Bibliographic record
Abstract
BackgroundBicuspid aortic valve (BAV) is associated with a faster progression of aortic stenosis (AS). Whether the determinants of AS progression are the same or different in patients with BAV vs tricuspid aortic valve (TAV) is unknown. The aim of this study was to identify the factors associated with the progression of AS in patients with BAV vs patients with TAV.MethodsPatients with AS were prospectively recruited in the Metabolic Determinants of the Progression of Aortic Stenosis (PROGRESSA) study (ClinicalTrials.gov Identifier: NCT01679431). The haemodynamic progression rate of AS was assessed by the annualized progression rate of peak aortic jet velocity (Vpeak). Univariable and multivariable linear regression analyses were used to identify the factors associated with a faster progression of AS in patients with BAV vs patients with TAV.ResultsThere were 79 patients with BAV and 208 patients with TAV. The baseline severity of AS was similar between the 2 groups of patients as well as the annualized progression rate of AS. In patients with BAV, obesity (β = 0.25, P = 0.04), diabetes (β = 0.26, P = 0.02), and BAV with right-noncoronary cusp fusion (β = 0.29, P = 0.01) were found to be independently associated with a faster progression of AS, whereas in patients with TAV, AS baseline severity (baseline Vpeak, β = 0.14, P = 0.04) and chronic kidney disease (β = 0.16, P = 0.02) were significantly associated with AS progression.ConclusionFactors associated with progression rate of AS are different in BAV and TAV. The main factors associated with a faster progression of AS appear to be obesity, diabetes, right-noncoronary cusp fusion in patients with BAV vs chronic kidney disease in patients with TAV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".