The Canadian Women’s Heart Health Alliance ATLAS on the Epidemiology, Diagnosis, and Management of Cardiovascular Disease in Women—Chapter 2: Scope of the Problem
Bibliographic record
Abstract
Background: This Atlas chapter summarizes the epidemiology of cardiovascular disease (CVD) in women in Canada, discusses sex and gender disparities, and examines the intersectionality between sex and other factors that play a prominent role in CVD outcomes in women, including gender, indigenous identity, ethnic variation, disability, and socioeconomic status. R ESUM EContexte : Ce chapitre de l'Atlas condense l' epid emiologie des maladies cardiovasculaires (MCV) chez les femmes au Canada, aborde les disparit es entre les sexes et les genres, et examine l'interrelation entre le sexe et d'autres facteurs qui jouent un rôle important dans l' emergence des MCV chez les femmes, notamment le genre, l'identit eCardiovascular disease (CVD) is the leading cause of premature death in women in Canada. 1 Beyond sex-unique CVD risk factors in women, several traditional risk factors have a greater morbidity and mortality impact in women compared to men.Rates of CVD vary substantially among provinces and within regions of Canada.This Atlas chapter aims to do the following: summarize the epidemiology of cardiovascular disease in women in Canada; discuss sex and gender disparities; and examine the intersectionality between sex and other disparities that play a prominent role in CVD outcomes in women, including indigenous identity, ethnic variation, disability, and socioeconomic status (SES).Figure 1 summarizes the key concepts presented in this chapter. Demographics Cardiovascular wellness indicators/traditional risk factorsMost Canadian women have at least one risk factor for CVD. 2 Although the burden of CVD has been improving over time, outcomes for women, particularly those aged < 55 years, have stagnated.3 Women are more likely than men to die in the year following an acute myocardial infarction (MI) and to experience death, heart failure, or stroke within 5 years after acute MI. 4,5 CJC Open 3 (2021) 1e11
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".