Cardio-rheumatology: the cardiovascular, pharmacological, and surgical risks associated with rheumatological diseases in women
Bibliographic record
Abstract
Cardiovascular disease (CVD) remains the number one cause of death worldwide. Women are at increased risk of death from CVD, but the mechanisms for how and why this occurs remain elusive. One subset of women who are exceptionally vulnerable to CVD are those with rheumatic diseases (RDs). Indeed, women account for 80% of all RDs, disorders that encompass a broad range of autoimmune and autoinflammatory diseases that lead to chronic inflammation and pathology. The clear association of increased CVD risk in women with RD is thought to be mediated by a number of factors, including RD pathology itself, pharmacological induction of CVD, and/or as yet unidentified mechanisms. As such, elucidation of the causes and treatments of these pathologies has given rise to a new subspecialty of cardiology: cardio-rheumatology. Here, we review and discuss the CVD risks in patients with RDs, the associated sex disparities in RD and CVD care, as well as the current therapeutic and interventional options available to specifically help women with RDs. We hope this discussion will provide guidance and support to patients, as well as to cardio-rheumatologists, as these groups are the most uniquely positioned to radically improve CVD care in these individuals. Moreover, we are hopeful this discussion may lead to better, more efficacious approaches to treating these disorders in women in the near future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".