Computed Tomography Aortic Valve Calcium Scoring in Patients With Bicuspid Aortic Valve Stenosis
Bibliographic record
Abstract
Background: Sex-specific thresholds of computed tomography (CT)-derived aortic valve calcification (AVC) or AVC density (AVCd) to identify severe aortic stenosis (AS) have been established in populations that consisted mainly of Caucasians with a tricuspid aortic valve. The objective of this study was to evaluate the accuracy (i.e., sensitivity and specificity) of previously established thresholds to identify severe AS in patients with bicuspid aortic valve (BAV) and according to ethnicity: Caucasian vs. Asian. Methods: We built a multicenter registry of echocardiographic and CT data collected in BAV patients with at least mild AS and preserved left ventricular ejection fraction from 7 different centers. Anatomic severity of AS obtained by CT-derived AVC and AVCd was compared to hemodynamic severity of AS obtained by echocardiography. Results: (Se/Spe = 86/80%) for AVCd. Conclusions: The optimal thresholds to identify hemodynamically severe AS in BAV patients are similar in Caucasians but appear to be higher in Asian men, compared with thresholds previously reported in tricuspid aortic valve patients. Nonetheless, the thresholds currently proposed in the guidelines have good accuracy and can be applied in BAV patients to confirm AS severity.
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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 | 0.003 |
| 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 | 0.000 |
| 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".