Real-world data and cardiovascular medications: diving into sex differences.
Bibliographic record
Abstract
Cardiovascular disease (CVD) in women remains understudied, under-recognized, underdiagnosed, and undertreated. Despite being the leading cause of death in women worldwide, and although more women die of CVD than men, studies have shown that women are less likely to benefit from the same care as men. What’s the extent of the differences? What factors explain it? Do the sex differences in cardiovascular care reflect different optimal treatment options or do they reflect suboptimal care in women? This work explored the sex differences in guideline-recommended treatment for CVD prevention and management. The findings highlight consistent sex differences in cardiovascular care, often to the disadvantage of women. Globally, women with acute coronary syndrome received less care than men. Zooming on clinical practice in the Netherlands, women had lower treatment and control of LDL cholesterol, were prescribed lower intensities of statins, were less likely to reach LDL-c targets or undergo treatment uptitration and more likely to discontinue statin therapy. Sex differences were also observed in the pharmacological management of hypertensive patients and suggest a need for identifying optimal dosing practice for each sex.
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
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 | 0.017 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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, 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".