Aortic Stenosis: Time for a Sex-Based Approach?
Bibliographic record
Abstract
Aortic stenosis (AS) is a progressive form of valvular heart disease most commonly associated with aging, with an exponential increase in prevalence after age 50. While men have historically been considered at higher risk, recent studies highlight a similar prevalence between men and women, with a higher prevalence in elderly women driven by longer life expectancy. Sex-related differences in clinical presentation, anatomy, and pathophysiology influence disease progression, severity assessment, and management. Women are often diagnosed at more advanced stages, exhibiting more pronounced symptoms, typically dyspnea and functional impairment, whereas men more often report chest pain. Women have a smaller body surface area, leading to smaller aortic annuli, left ventricular outflow tracts, aortic roots impacting flow dynamic, and severity grading. Diagnostic challenge contributes to the undertreatment of women. Despite experiencing severe AS, women receive fewer interventions and face delays in treatment. The advent of transcatheter aortic valve implantation (TAVI) improved outcomes, with studies suggesting a potential advantage in women compared to men. However, the anatomical differences, such as smaller annuli and more tortuous vascular access, necessitate tailored procedural approaches. Recognizing these sex-specific differences is essential to optimizing AS management, ensuring timely interventions, and improving patient outcomes. Future strategies should incorporate sex-specific thresholds for diagnosis and treatment while leveraging technological advancements, such as artificial intelligence, for personalized therapeutic decisions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".