The Canadian Women’s Heart Health Alliance Atlas on the Epidemiology, Diagnosis, and Management of Cardiovascular Disease in Women — Chapter 6: Sex- and Gender-Specific Diagnosis and Treatment
Bibliographic record
Abstract
This chapter summarizes the sex- and gender-specific diagnosis and treatment of acute/unstable presentations and nacute/stable presentations of cardiovascular disease in women. Guidelines, scientific statements, systematic reviews/meta-analyses, and primary research studies related to diagnosis and treatment of coronary artery disease, cerebrovascular disease (stroke), valvular heart disease, and heart failure in women were reviewed. The evidence is summarized as a narrative, and when available, sex- and gender-specific practice and research recommendations are provided. Acute coronary syndrome presentations and emergency department delays are different in women than they are in men. Coronary angiography remains the gold-standard test for diagnosis of obstructive coronary artery disease. Other diagnostic imaging modalities for ischemic heart disease detection (eg, positron emission tomography, echocardiography, single-photon emission computed tomography, cardiovascular magnetic resonance, coronary computed tomography angiography) have been shown to be useful in women, with their selection dependent upon both the goal of the individualized assessment and the testing resources available. Noncontrast computed tomography and computed tomography angiography are used to diagnose stroke in women. Although sex-specific differences appear to exist in the efficacy of standard treatments for diverse presentations of acute coronary syndrome, many cardiovascular drugs and interventions tested in clinical trials were not powered to detect sex-specific differences, and knowledge gaps remain. Similarly, although knowledge is evolving about sex-specific difference in the management of valvular heart disease, and heart failure with both reduced and preserved ejection fraction, current guidelines are lacking in sex-specific recommendations, and more research is needed.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".