Abstract 341: Optimizing Cardiovascular Disease Research in Women
Bibliographic record
Abstract
Cardiovascular (CV) disease (CVD) is the major health burden and cause of death for women. Marked disparities exist in CVD diagnosis, prevention, and treatment between women and men – as well as lack of female-specific data. Population, physiologic, translational, and clinical trial studies of sex and gender differences in CVD [[Unable to Display Character: –]] even when only women are studied[[Unable to Display Character: –]] often do not collect relevant data specific to women that could inform study outcomes. The ISIS CVD Network of the Society for Women’s Health Research compiled an inventory of items specific for women across the lifespan, together with references for methods and strategies to gather and evaluate this information; some items comprise robust measures, others are in development. The objective is to enhance usefulness of CVD research data in understanding sex and gender differences, thereby optimizing healthcare delivery and outcomes for women. Included are hormonal variables (menstrual cycle phase, hormone levels) oral contraceptive use, pregnancy history/complications, polycystic ovary syndrome (PCOS) components, measures of menopause, and menopausal hormone therapy, variables generally not collected in research studies, but essential to determine their role as sex-specific contributors to CV health and disease. Clear associations exist between reproductive health and CV health and disease. For example 25-33% of women experience complications of pregnancy that may precede and predispose to CVD. Vascular complications during pregnancy, antecedent risk factors and subsequent clinical CVD can be ascertained using medical records, birth registries, and/or maternal recall. Evaluating compilations of patient data with known hormonal or menopausal status using reference standards and patient data for PCOS could inform relationships to subsequent CV outcomes. Variables predominant among women that preferentially disadvantage them should be considered; e.g. psychosocial issues and elderly age. Depressive disorders are twice as common among women as men. They adversely affect CVD outcomes in women, yet the effect of reproductive life cycle and of hormonal fluctuations on depression and etiologic contributions of depression to CVD are inadequately explored. In addition to traditional CVD risk factors, diabetes mellitus, chronic inflammatory disorders, oxidative stress, vasomotor dysfunction, coronary microvascular disorders, and other novel risk variables that preferentially impact women should be explored. Along with increased enrollment of women in CVD research studies and analysis of clinical and genetic studies by sex, improvements and expansion of study design must include these understudied uniquely or predominantly female characteristics. This will enhance the quality and quantity of evidence-based medicine to guide CVD care in women and men thereby setting the stage for personalized approach to medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".