Abstract 13512: Sex-related Differences on Valvular Calcifications in Patients With Aortic Stenosis
Bibliographic record
Abstract
Introduction: It has been shown that women present lower coronary artery (CAC) and aortic valve calcification (AVC) loads while heavier mitral annular calcification (MAC) than men. However, the sex-specific predictors to these cardiac calcifications remain poorly characterized. Methods: We conducted a cross-sectional study in patients with at least mild AS (indexed aortic valve area: AVAi < 1.5 cm 2 /m 2 , Peak aortic jet velocity: Vpeak > 2.0 m/s, or Mean gradient: MG >15 mmHg). Doppler-echocardiography and non-contrast multidetector compute tomography were performed within 3 months. Ascending aorta calcification (AAC), AVC, CAC and MAC scores were measured using the Agatston method. Descriptive statistical analyses (t-test, Wilcoxon, univariate and multivariate analysis) were performed. Results: We studied 406 patients (71±11 years, 33% women) with AVAi= 0.59±0.21 cm 2 /cm 2 , Vpeak= 3.1±9.8 m/s, MG= 24.7±17.8 mmHg (equivalent between men and women, all p>0.34). Women present less AVC (480[222-1191] vs 1005[485-2364]AU; p<0.0001), and CAC (366[50-914] vs 626[167-1354]AU; p=0.006), but more MAC (60[1-887] vs 48[0-363]AU; p=0.05) and AAC (227[43-863] vs 142[7-493]AU; p=0.03) than men. Even after comprehensive adjustment, sex remained an independent predictor of each cardiac calcification (all p<0.01). In multivariate analysis, correlates with higher AVC or higher MAC were sex dependent (cf. table). Collinearity was avoided with all variance inflating factor <2.5. Conclusion: In AS patients, sex is a powerful and independent predictor of cardiac calcifications. Moreover, predictors of valvular calcification appear to be sex specific.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".