A comprehensive view of sex-specific issues related to cardiovascular disease
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of mortality in women. In fact, CVD is responsible for a third of all deaths of women worldwide and half of all deaths of women over 50 years of age in developing countries. The prevalence of CVD risk factor precursors is increasing in children. Retrospective analyses suggest that there are some clinically relevant differences between women and men in terms of prevalence, presentation, management and outcomes of the disease, but little is known about why CVD affects women and men differently. For instance, women with diabetes have a significantly higher CVD mortality rate than men with diabetes. Similarly, women with atrial fibrillation are at greater risk of stroke than men with atrial fibrillation. Historically, women have been underrepresented in clinical trials. The lack of good trial evidence concerning sex-specific outcomes has led to assumptions about CVD treatment in women, which in turn may have resulted in inadequate diagnoses and suboptimal management, greatly affecting outcomes. This knowledge gap may also explain why cardiovascular health in women is not improving as fast as that of men. Over the last decades, mortality rates in men have steadily declined, while those in women remained stable. It is also becoming increasingly evident that gender differences in cultural, behavioural, psychosocial and socioeconomic status are responsible, to various degrees, for the observed differences between women and men. However, the interaction between sex-and gender-related factors and CVD outcomes in women remains largely unknown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".