Determinants of Aortic Stenosis Progression in Bicuspid and Tricuspid Aortic Valves
Bibliographic record
Abstract
Background Bicuspid 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. Methods Patients 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 (V peak ). 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. Results There 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 V peak , β = 0.14, P = 0.04) and chronic kidney disease (β = 0.16, P = 0.02) were significantly associated with AS progression. Conclusion Factors 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 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 | 0.001 |
| 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".