Understanding Thoracic Aortic Disease in Women
Bibliographic record
Abstract
Multifaceted disparities exist between men and women with thoracic aortic aneurysm and dissection. Despite a higher prevalence of thoracic aortic aneurysm and dissection among men, women experience disproportionately accelerated aneurysmal expansion, greater risks of rupture or dissection, and acute aortic syndromes that occur at relatively smaller diameters. In the context of acute type A aortic dissection, they also experience more complications, increased out-of-hospital mortality, delays in presentation and diagnosis, and worse postoperative survival. These gaps are largely driven by sex differences in vascular aging and remodeling, which include arterial stiffening associated with the hormonal changes that occur during menopause. Furthermore, the increased risk of acute type A aortic dissection during pregnancy in women with thoracic aortic disease necessitates a multidisciplinary approach to peripartum counseling and surveillance. Despite significant recent improvements in early postoperative outcomes, other disparities persist, emphasizing the need for sex-specific research, patient counseling, routine monitoring, and surgical thresholds to bridge the gap in outcomes of thoracic aortic care between sexes. Elucidating the underlying mechanisms of aortic aging and its difference between men and women, as well as moving toward personalized management protocols, will give rise to improved outcomes in the treatment of thoracic aortopathy.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".