The Canadian Women’s Heart Health Alliance ATLAS on the Epidemiology, Diagnosis, and Management of Cardiovascular Disease in Women — Chapter 8: Knowledge Gaps and Status of Existing Research Programs in Canada
Bibliographic record
Abstract
Despite significant progress in medical research and public health efforts, gaps in knowledge of women's heart health remain across epidemiology, presentation, management, outcomes, education, research, and publications. Historically, heart disease was viewed primarily as a condition in men and male individuals, leading to limited understanding of the unique risks and symptoms that women experience. These knowledge gaps are particularly problematic because globally heart disease is the leading cause of death for women. Until recently, sex and gender have not been addressed in cardiovascular research, including in preclinical and clinical research. Recruitment was often limited to male participants and individuals identifying as men, and data analysis according to sex or gender was not conducted, leading to a lack of data on how treatments and interventions might affect female patients and individuals who identify as women differently. This lack of data has led to suboptimal treatment and limitations in our understanding of the underlying mechanisms of heart disease in women, and is directly related to limited awareness and knowledge gaps in professional training and public education. Women are often unaware of their risk factors for heart disease or symptoms they might experience, leading to delays in diagnosis and treatments. Additionally, health care providers might not receive adequate training to diagnose and treat heart disease in women, leading to misdiagnosis or undertreatment. Addressing these knowledge gaps requires a multipronged approach, including education and policy change, built on evidence-based research. In this chapter we review the current state of existing cardiovascular research in Canada with a specific focus on women.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.032 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".