MétaCan
Menu
Back to cohort
Record W4387815690 · doi:10.1016/j.cjco.2023.10.012

Sex, Gender, and Women's Heart Health: How Women's Heart Programs Address the Knowledge Gap

2023· review· en· W4387815690 on OpenAlexaffabout
Mahraz Parvand, Siavash Ghadiri, Emilie T. Théberge, Lisa Comber, Kerri‐Anne Mullen, Natasha Prodan Bhalla, Denise Johnson, Gayl McKinley, Tara Sedlak

Bibliographic record

VenueCJC Open · 2023
Typereview
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsB.C. Women's Hospital & Health CentreCanadian Heart Research CentreVancouver General HospitalOttawa Heart InstituteUniversity of British Columbia
Fundersnot available
KeywordsMedicineMultidisciplinary approachCardiovascular healthHealth careGerontologyGender gapFamily medicineNursingDiseasePolitical science

Abstract

fetched live from OpenAlex

This article aims to bridge existing knowledge gaps that impact clinical cardiovascular care and outcomes for women in Canada. The authors discuss various aspects of women's heart health, emphasizing the efficacy of multidisciplinary care in promoting women's well-being. The article also identifies the impact of national women's heart health campaigns and the value of peer support in improving outcomes. The article addresses the particular risks that women face, such as pregnancy-related complications and hormone replacement therapy, all of which are associated with cardiovascular events, and highlights the differences in ischemic symptoms between men and women. Despite improvements in acute event outcomes, challenges persist in accessing timely ambulatory care, particularly for women. Canada has responded to these challenges by introducing Women Heart Programs, which offer tailored programs, support groups, and specialized testing. However, these programs remain few in number and are found only in urban settings. Overall, this review identifies sex and gender factors related to women's heart health, underscoring the importance of specialized programs and multidisciplinary care in improving women's cardiovascular health.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.166
GPT teacher head0.416
Teacher spread0.250 · 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 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

Citations8
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCJC OpenSame topicCardiovascular Issues in PregnancyFrench-language works237,207