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Record W4408388866 · doi:10.1161/circresaha.124.325681

Understanding Thoracic Aortic Disease in Women

2025· review· en· W4408388866 on OpenAlexaff
Bana Samman, Mimi Deng, Jennifer Chung, Maral Ouzounian

Bibliographic record

VenueCirculation Research · 2025
Typereview
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineThoracic aortic aneurysmContext (archaeology)Aortic dissectionAortic aneurysmDissection (medical)CardiologyAneurysmDiseaseThoracic aortaPregnancyInternal medicineSurgeryAorta

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.542
GPT teacher head0.524
Teacher spread0.018 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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