Grading of Aortic Valve Calcification Severity and Risk Stratification in Aortic Stenosis
Bibliographic record
Abstract
Background Thresholds of aortic valve calcification (AVC) to define hemodynamically moderate aortic stenosis (AS) from mild are lacking. We aimed to establish a novel grading classification of AVC as quantified by computed tomography and determine its prognostic value. Methods and Results This study included 915 patients with at least mild AS (mean age 70±12 years, 30% women) from a multicenter prospective registry. All patients underwent Doppler‐echocardiography and noncontrast computed tomography within 3 months. Primary end point was the occurrence of all‐cause death. Receiver operating characteristic curves analyses were used to determine the sensitivity and specificity of sex‐specific thresholds of AVC to identify hemodynamically moderate AS. Optimal thresholds (ie, with best sensitivity/specificity) of AVC to distinguish moderate (aortic valve area 1.0–1.5 cm 2 and mean gradient 20–39 mm Hg) from mild AS (aortic valve area >1.5 cm 2 and mean gradient <20 mm Hg) were AVC ≥360 arbitrary units in women and ≥1037 arbitrary units in men. Based on the guidelines' thresholds for severe AS and the new thresholds in our study for moderate AS, 312 (34%) patients had mild, 253 (28%) moderate, and 350 (38%) severe AVC. During a mean follow‐up of 5.6±3.9 years, 183 (27%) deaths occurred. In Cox multivariable models, AVC remained associated with an increased risk of death (adjusted hazard ratio per grade increase, 1.94 [95% CI, 1.53–2.56]; P <0.001). Conclusions A novel grading classification of anatomic AS severity based on sex‐specific thresholds of AVC provides significant prognostic value for predicting mortality. These findings support the complementarity of computed tomography‐calcium scoring to Doppler‐echocardiography to corroborate AS severity and enhance risk stratification in patients with AS.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".