Lifelong Management of Aortic Stenosis in Young Women: A Comprehensive Review
Bibliographic record
Abstract
Despite similar burdens of aortic stenosis (AS) in men and women, management guidelines have historically focused on older, male populations. Age and sex differences contribute to variances in symptomatology and postprocedural outcomes. This review aims to evaluate strategies for lifelong management of AS in young women, specifically during childbearing age and pregnancy, and in women with failed bioprosthetic valves. Advanced surgical options such as the Ross and Ozaki Procedures, in addition to transcatheter aortic valve repair (TAVR) and traditional surgical aortic valve repair (SAVR), are viable options for young women with AS. However, valve durability and reintervention rates for each procedure remain major factors that affect decision-making. Maternal and fetal complications are of paramount concern when treating pregnant patients. Effects of altered uterine-placental blood flow and the risk of intraprocedural fetal irradiation should be considered when choosing an intervention for these patients, and efforts should be made to minimize risks to both mother and fetus. Current options for young women with failed bioprosthesis include redo-SAVR, TAVR in SAVR, TAVR in TAVR, and redo-SAVR after TAVR. These options are often associated with high rates of procedural complications and pitfalls compared to initial intervention in young women. No single treatment modality emerges as the best option for young women with AS. Ultimately, care should be tailored to each young woman by considering their clinical condition, reproductive goals, personal preferences, and associated comorbidities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".