Additive Value of CT to Age, Aortic Diameter, and Echocardiography in Diagnosis and Classification of Bicuspid Aortic Valve in Patients with Severe Aortic Stenosis
Bibliographic record
Abstract
Purpose To develop and validate a CT diagnostic algorithm for bicuspid aortic valve (BAV) classification. Materials and Methods This retrospective study included 212 consecutive patients with severe aortic stenosis who underwent CT followed by aortic valve replacement (mean age, 71 years [range, 27–93 years]; 125 women; 37% with a BAV) from 2012 to 2017. BAV diagnosis and BAV category were determined by using the CT diagnostic algorithm developed and were compared with those attained through surgical diagnosis. Reproducibility and agreement were assessed using the Cohen kappa (κ) coefficient. The value of adding CT to age, aortic diameter index, and transthoracic echocardiography (TTE) was evaluated by using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and decision-curve analysis. Results Intra- and interobserver reproducibility were good or excellent for all CT diagnoses (κ ≥ 0.6 for all). Agreement between CT and surgical diagnoses was excellent (κ = 0.90) for BAV detection and good (κ = 0.69) for BAV categorization. Sixteen percent (five of 31) of patients with functional BAV diagnosed by using CT received a diagnosis of congenital BAV at surgery. The addition of CT to age, aortic diameter, and TTE showed a higher AUC (with CT, 0.97 [95% CI: 0.91, 0.99] vs without CT, 0.91 [95% CI: 0.85, 0.95]; P = .003) and NRI (1.79 [95% CI: 1.65, 1.92], P < .001) and a higher net benefit among all BAV probabilities. Conclusion CT diagnosis was consistent with surgical diagnosis and had an additive value over traditional diagnostic methods; however, there was a risk of overlooking congenital BAV in patients with functional BAV diagnosed by using CT. Supplemental material is available for this article. Keywords: Adults, Aortic Valve, CT-Angiography, Cardiac © RSNA, 2021
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".