Age, Sex, and Valve Phenotype Differences in Fibro‐Calcific Remodeling of Calcified Aortic Valve
Bibliographic record
Abstract
Background In calcific aortic valve disease on tricuspid aortic valves ( TAVs ), men have higher aortic valve calcification and less fibrosis than women. However, little is known in bicuspid aortic valves ( BAV ). We thus aimed to investigate the impact of age, sex, and valve phenotype ( TAVs versus BAVs ) on fibro‐calcific remodeling in calcific aortic valve disease. Methods and Results We included 2 cohorts: 411 patients who underwent multidetector computed tomography (37% women) for aortic valve calcification density assessment and 138 explanted aortic valves (histological cohort; 50% women). The cohorts were divided in younger (<60 years old) or older patients with BAV (≥60 years old), and TAV patients. In each group, women and men were matched. Women presented less aortic valve calcification density than men in each group of the multidetector computed tomography cohort (all P ≤0.01). Moreover, in women, younger patients with BAV had the lowest aortic valve calcification density (both P =0.02). In multivariate analysis, aortic valve calcification density correlated with age (β estimate±standard error: 6.5±1.8; P =0.0004) and male sex (109.2±18.4; P <0.0001), and there was a trend with TAVs (41.5±23.0; P =0.07). Women presented a higher collagen content than men (77.8±10.8 versus 69.9±12.9%; P <0.001) in the entire cohort. In women, younger patients with BAV had denser connective tissue than TAV and older patients with BAV (both P ≤0.05), while no difference was observed between men. Conclusions In calcific aortic valve disease, women had less calcification and more fibrotic remodeling than men, regardless of the phenotype of the valve or age of the patient. Moreover, younger women with BAVs had less valve calcification. Thus, mineralization/fibrosis of the aortic valve is likely to have sex/age‐specific mechanisms and be influenced by the valve morphology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".