Differences in Presentation, Diagnosis and Management of Heart Failure in Women. A Scientific Statement of the Heart Failure Association of the ESC
Bibliographic record
Abstract
Despite the progress in the care of individuals with heart failure (HF), important sex disparities in knowledge and management remain, covering all the aspects of the syndrome, from aetiology and pathophysiology to treatment. Important distinctions in phenotypic presentation are widely known, but the mechanisms behind these differences are only partially defined. The impact of sex-specific conditions in the predisposition to HF has gained progressive interest in the HF community. Under-recruitment of women in large randomized clinical trials has continued in the more recent studies despite epidemiological data no longer reporting any substantial difference in the lifetime risk and prognosis between sexes. Target dose of medications and criteria for device eligibility are derived from studies with a large predominance of men, whereas specific information in women is lacking. The present scientific statement encompasses the whole scenario of available evidence on sex-disparities in HF and aims to define the most challenging and urgent residual gaps in the evidence for the scientific and clinical HF communities.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".