Computed Tomography Aortic Valve Calcium Scoring in Patients With Aortic Stenosis
Bibliographic record
Abstract
Background— Computed tomography aortic valve calcium scoring (CT-AVC) holds promise for the assessment of patients with aortic stenosis (AS). We sought to establish the clinical utility of CT-AVC in an international multicenter cohort of patients. Methods and Results— Patients with AS who underwent ECG-gated CT-AVC within 3 months of echocardiography were entered into an international, multicenter, observational registry. Optimal CT-AVC thresholds for diagnosing severe AS were determined in patients with concordant echocardiographic assessments, before being used to arbitrate disease severity in those with discordant measurements. In patients with long-term follow-up, we assessed whether CT-AVC thresholds predicted aortic valve replacement and death. In 918 patients from 8 centers (age, 77±10 years; 60% men; peak velocity, 3.88±0.90 m/s), 708 (77%) patients had concordant echocardiographic assessments, in whom CT-AVC provided excellent discrimination for severe AS (C statistic: women 0.92, men 0.89). Our optimal sex-specific CT-AVC thresholds (women 1377 Agatston unit and men 2062 Agatston unit) were nearly identical to those previously reported (women 1274 Agatston unit and men 2065 Agatston unit). Clinical outcomes were available in 215 patients (follow-up 1029 [126–2251] days). Sex-specific CT-AVC thresholds independently predicted aortic valve replacement and death (hazard ratio, 3.90 [95% confidence interval, 2.19–6.78]; P <0.001) after adjustment for age, sex, peak velocity, and aortic valve area. Among 210 (23%) patients with discordant echocardiographic assessments, there was considerable heterogeneity in CT-AVC scores, which again were an independent predictor of clinical outcomes (hazard ratio, 3.67 [95% confidence interval, 1.39–9.73]; P =0.010). Conclusions— Sex-specific CT-AVC thresholds accurately identify severe AS and provide powerful prognostic information. These findings support their integration into routine clinical practice. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifiers: NCT01358513, NCT02132026, NCT00338676, NCT00647088, NCT01679431.
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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 | 0.004 |
| 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 | 0.001 | 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".