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Record W3001966161 · doi:10.1093/cvr/cvaa001

Sex differences in non-obstructive coronary artery disease

2020· review· en· W3001966161 on OpenAlexaff
Nida Waheed, Suzette Elias‐Smale, Waddah Malas, Angela H.E.M. Maas, Tara Sedlak, Jennifer A. Tremmel, Puja K. Mehta

Bibliographic record

VenueCardiovascular Research · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsBritish Columbia Centre of Excellence for Women's HealthUniversity of British Columbia
Fundersnot available
KeywordsMedicineCoronary artery diseaseCardiologyInternal medicineAnginaEndothelial dysfunctionVasomotorPathophysiologyFractional flow reserveIschemiaMyocardial infarctionCoronary angiography

Abstract

fetched live from OpenAlex

Ischaemic heart disease is a leading cause of morbidity and mortality in both women and men. Compared with men, symptomatic women who are suspected of having myocardial ischaemia are more likely to have no obstructive coronary artery disease (CAD) on coronary angiography. Coronary vasomotor disorders and coronary microvascular dysfunction (CMD) have been increasingly recognized as important contributors to angina and adverse outcomes in patients with no obstructive CAD. CMD from functional and structural abnormalities in the microvasculature is associated with adverse cardiac events and mortality in both sexes. Women may be particularly susceptible to vasomotor disorders and CMD due to unique factors such as inflammation, mental stress, autonomic, and neuroendocrine dysfunction, which predispose to endothelial dysfunction and CMD. CMD can be detected with coronary reactivity testing and non-invasive imaging modalities; however, it remains underdiagnosed. This review focuses on sex differences in presentation, pathophysiologic risk factors, diagnostic testing, and prognosis of CMD.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.104
GPT teacher head0.384
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations119
Published2020
Admission routes1
Has abstractyes

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