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Record W4386776229 · doi:10.1016/j.cjco.2023.09.006

Female-Specific Considerations in Aortic Health and Disease

2023· article· en· W4386776229 on OpenAlexaff
Rebecca Crosier, Maria Agustina Lopez Laporte, Rudy R Unni, Thais Coutinho

Bibliographic record

VenueCJC Open · 2023
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineDiseaseArterial stiffnessAbdominal aortaCardiologyAortaInternal medicineOxidative stressElastinThoracic aortaSenescenceVentricleStressorPhysiologyPathologyBlood pressure

Abstract

fetched live from OpenAlex

The aorta plays a central role in the modulation of blood flow to supply end organs and to optimize the workload of the left ventricle. The constant interaction of the arterial wall with protective and deleterious circulating factors, and the cumulative exposure to ventriculoarterial pulsatile load, with its associated intimal-medial changes, are important players in the complex process of vascular aging. Vascular aging is also modulated by biomolecular processes such as oxidative stress, genomic instability, and cellular senescence. Concomitantly with well-established cardiometabolic and sex-specific risk factors and environmental stressors, arterial stiffness is associated with cardiovascular disease, which remains the leading cause of morbidity and mortality in women worldwide. Sexual dimorphisms in aortic health and disease are increasingly recognized and explain-at least in part-some of the observable sex differences in cardiovascular disease, which will be explored in this review. Specifically, we will discuss how biological sex affects arterial health and vascular aging and the implications this has for development of certain cardiovascular diseases uniquely or predominantly affecting women. We will then expand on sex differences in thoracic and abdominal aortic aneurysms, with special considerations for aortopathies in pregnancy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.074
GPT teacher head0.368
Teacher spread0.294 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2023
Admission routes1
Has abstractyes

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