Sex, Drugs, and Heart Failure: A Sex-Sensitive Review of the Evidence Base Behind Current Heart Failure Clinical Guidelines
Bibliographic record
Abstract
Heart failure (HF) is a complex disease, almost as common in women as in men. Nonetheless, HF clinical presentation, prognosis, and aetiology vary by sex. This review summarizes the current state of sex-sensitive issues related to HF drugs included in treatment guidelines and suggests future directions for improved care. Heart failure presentation differs between female and male patients: females more often show with hypertensive aetiology and the preserved ejection fraction phenotype, while men more often show ischaemic aetiology and the reduced ejection fraction phenotype. Yet the HF clinical guidelines in Europe, the United States, and Canada do not reflect the sexual dimorphism. Further, in randomized clinical trials of HF medication, women are largely underrepresented, typically consisting of ≥70% men. Given the knowledge that some adverse drug reactions, such as torsade de pointes and angiotensin-converting enzyme inhibitor-induced cough, occur more frequently in women, we emphasize the need to test medications thoroughly in both sexes and explore sexual dimorphisms. To better represent all of the targeted patient population and provide better care for all, two kinds of change must come about: recruitment methods to randomized clinical trial samples need to evolve and the participation needs to seem more attractive to 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".