Performance of Computed Tomographic Angiography–Based Aortic Valve Area for Assessment of Aortic Stenosis
Bibliographic record
Abstract
Background A total of 40% of patients with severe aortic stenosis (AS) have low‐gradient AS, raising uncertainty about AS severity. Aortic valve calcification, measured by computed tomography (CT), is guideline‐endorsed to aid in such cases. The performance of different CT‐derived aortic valve areas (AVAs) is less well studied. Methods and Results Consecutive adult patients with presumed moderate and severe AS based on echocardiography (AVA measured by continuity equation on echocardiography <1.5 cm 2 ) who underwent cardiac CT were identified retrospectively. AVAs, measured by direct planimetry on CT (AVA CT ) and by a hybrid approach (AVA measured in a hybrid manner with echocardiography and CT [AVA Hybrid ]), were measured. Sex‐specific aortic valve calcification thresholds (≥1200 Agatston units in women and ≥2000 Agatston units in men) were applied to adjudicate severe or nonsevere AS. A total of 215 patients (38.0% women; mean±SD age, 78±8 years) were included: normal flow, 59.5%; and low flow, 40.5%. Among the different thresholds for AVA CT and AVA Hybrid , diagnostic performance was the best for AVA CT <1.2 cm 2 (sensitivity, 85%; specificity, 26%; and accuracy, 72%), with no significant difference by flow status. The percentage of patients with correctly classified AS severity (correctly classified severe AS+correctly classified moderate AS) was as follows; AVA measured by continuity equation on echocardiography <1.0 cm 2 , 77%; AVA CT <1.2 cm 2 , 73%; AVA CT <1.0 cm 2 , 58%; AVA Hybrid <1.2 cm 2 , 59%; and AVA Hybrid <1.0 cm 2 , 45%. AVA CT cut points of 1.52 cm 2 for normal flow and 1.56 cm 2 for low flow, provided 95% specificity for excluding severe AS. Conclusions CT‐derived AVAs have poor discrimination for AS severity. Using an AVA CT <1.2‐cm 2 threshold to define severe AS can produce significant error. Larger AVA CT thresholds improve specificity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".