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Sex Differences in Carotid Plaque and Stenosis

2004· article· en· W2151993143 on OpenAlexaff
Francesco Iemolo, Alexandra Martiniuk, David A. Steinman, J. David Spence

Bibliographic record

VenueStroke · 2004
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsThrombosis and Atherosclerosis Research InstituteWestern University
Fundersnot available
KeywordsMedicineStenosisInternal medicineCardiologyStroke (engine)Myocardial infarctionHormone replacement therapy (female-to-male)EstrogenEpidemiologyTestosterone (patch)

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Women are relatively protected from cardiovascular events; they are 3 times as likely as men to survive to age 90 years. Although clinical trials show an excess of thrombotic events with estrogen/progestin hormone replacement therapy, much experimental and epidemiological evidence suggests that estrogen may have beneficial effects on endothelial function and atherosclerosis, raising the possibility of sex differences in arterial remodeling. We studied sex differences in carotid plaque and stenosis in relation to survival free of stroke, death, and myocardial infarction. METHODS: A total of 1686 patients from an atherosclerosis prevention clinic were followed annually for up to 5 years (mean, 2.5+/-1.3 years) with baseline and follow-up measurements; there were 45 strokes, 94 myocardial infarctions, and 41 deaths. RESULTS: Carotid stenosis and plaque increased with age. Women had greater stenosis compared with men (P=0.001), whereas men had greater plaque area than did women at all ages (P<0.0001). Stroke, myocardial infarction, and death combined were predicted significantly by plaque area (P=0.004) but not by stenosis (P=0.042). CONCLUSIONS: Women have more stenosis but less plaque than men, suggesting that differences in sex hormones may affect remodeling of atherosclerosis. Plaque area was a stronger predictor of outcomes than was stenosis.

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 score0.004
Threshold uncertainty score0.272

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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

Citations160
Published2004
Admission routes1
Has abstractyes

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