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Record W4318613934 · doi:10.1097/hco.0000000000001012

Gender differences in acute aortic syndromes

2023· review· en· W4318613934 on OpenAlexaff
Nitish Bhatt, Jennifer Chung

Bibliographic record

VenueCurrent Opinion in Cardiology · 2023
Typereview
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineAortic dissectionAcute aortic syndromePresentation (obstetrics)Ascending aortaInternal medicinePopulationDiseaseCardiologyCohortAortaPediatricsSurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Acute aortic syndromes include acute aortic dissection, intramural hematoma, and penetrating aortic ulcer, and are associated with high mortality and morbidity. This review focuses on recent findings and current understanding of gender-related and sex-related differences in acute aortic syndromes. RECENT FINDINGS: Large international and national registries, population studies, and multicentre national prospective cohort studies show evidence of sex differences in acute aortic syndromes. Recent studies of risk factors, aorta remodelling, and genetics provide possible biological basis for sex differences. The 2022 American College of Cardiology/American Heart Association Guidelines for the Diagnosis and Management of Aortic Disease revise recommendations for surgical management for aortic root and ascending aorta dilatation, which could impact outcome differences between the sexes. SUMMARY: Acute aortic syndromes affect men more frequently than women. The prevalence of acute aortic syndromes and prevalence of many risk factors rise sharply with age in women leading to higher age at presentation for women. Times from symptom onset to presentation and presentation to diagnosis are delayed in female patients. Females with type A dissection are also more commonly treated conservatively than male counterparts. These factors likely contribute to higher early mortality and complications in women.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
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 head0.377
GPT teacher head0.455
Teacher spread0.078 · 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.

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

Citations12
Published2023
Admission routes1
Has abstractyes

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