Coronary artery disease in post-menopausal women: are there appropriate means of assessment?
Bibliographic record
Abstract
The recognition of sex differences in cardiovascular disease, particularly the manifestations of coronary artery disease (CAD) in post-menopausal women, has introduced new challenges in not only understanding disease mechanisms but also identifying appropriate clinical means of assessing the efficacy of management strategies. For example, the majority of treatment algorithms for CAD are derived from the study of males, focus on epicardial stenoses, and inadequately account for the small intramyocardial vessel disease in women. However, newer investigational modalities, including stress perfusion cardiac magnetic resonance imaging and positron emission tomography are providing enhanced diagnostic accuracy and prognostication for women with microvascular disease. Moreover, these investigations may soon be complemented by simpler screening tools such as retinal vasculature imaging, as well as novel biomarkers (e.g. heat shock protein 27). Hence, it is vital that robust, sex-specific cardiovascular imaging modalities and biomarkers continue to be developed and are incorporated into practice guidelines that are used to manage women with CAD, as well as gauge the efficacy of any new treatment modalities. This review provides an overview of some of the sex differences in CAD and highlights emerging advances in the investigation of CAD in post-menopausal women.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".