Unique cardiometabolic factors in women that contribute to modified cardiovascular disease risk
Bibliographic record
Abstract
Major risk factors of cardiovascular disease (CVD) include hypertension, obesity, diabetes mellitus and metabolic syndrome; all of which are considered inflammatory conditions. Women are disproportionately affected by inflammatory conditions, with sex differences emerging as early as adolescence. Hormonal fluctuations associated with reproductive events such as menarche, pregnancy and menopause, are hypothesized to promote a pro-inflammatory state in women. Moreover, women who have experienced inflammatory-type conditions such as polycystic ovarian syndrome (PCOS), gestational diabetes or pre-eclampsia, have a cardiometabolic phenotype that pre-disposes to increased risk of myocardial infarction, stroke and coronary heart disease. Women with no notable CVD risk factors are often relatively protected from CVD pre-menopause; but overtake men in risk of major cardiovascular events when the cardiovascular protective effects of oestrogen begin to wane. Sex differences and female-specific factors have long been considered challenging to study and this has led to an underrepresentation of females in clinical trials and lack of female-specific data from pre-clinical studies. However, there is now a clear prerogative to include females at all stages of research, despite inherent complexities and potential variability in data. This review explores recent advancements in our understanding of CVD in women. We summarise the underlying factors unique to women that can promote CVD risk factors, ultimately contributing to CVD burden and the emerging therapies aimed to combat this.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".