Abstract 341: Optimizing Cardiovascular Disease Research in Women
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».