HFpEF and sex: understanding the role of sex differences
Bibliographic record
Abstract
Heart failure is a complex clinical syndrome with many etiological factors and complex pathophysiology affecting millions worldwide. Males and females can have distinct clinical presentation and prognosis, and there is an emerging understanding of the factors that highlight the similarities and differences to synthesize and present available data for sex-specific differences in heart failure with preserved ejection fraction (HFpEF). While the majority of data demonstrate more similarities than differences between females and males in terms of heart failure, there are key differences. Data showed that females have a higher risk of developing HFpEF, but a lower risk of mortality and hospitalization. This can be conditioned by different profiles of comorbidities, postmenopausal changes in sex hormone levels, higher levels of inflammation and chronic microvascular dysfunction in females. These factors, combined with different left ventricular dimensions and function, which are more pronounced with age, lead to a higher prevalence of LV diastolic dysfunction at rest and exercise. As a result, females have lower exercise capacity and quality of life when compared to males. Females also have different activities of systems responsible for drug transformation, leading to different efficacy of drugs as well as higher risk of adverse drug reactions. These data prove the necessity for creating sex-specific risk stratification scales and treatment plans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".