Adverse Drug Reactions to Guideline-Recommended Heart Failure Drugs in Women
Bibliographic record
Abstract
OBJECTIVES: This study sought to summarize all available evidence on sex differences in adverse drug reactions (ADRs) to heart failure (HF) medication. BACKGROUND: Women are more likely to experience ADRs than men, and these reactions may negatively affect women's immediate and long-term health. HF in particular is associated with increased ADR risk because of the high number of comorbidities and older age. However, little is known about ADRs in women with HF who are treated with guideline-recommended drugs. METHODS: A systematic search of PubMed and EMBASE was performed to collect all available information on ADRs to angiotensin-converting enzyme inhibitors, β-blockers, angiotensin II receptor blockers, mineralocorticoid receptor antagonists, ivabradine, and digoxin in both women and men with HF. RESULTS: The search identified 155 eligible records, of which only 11 (7%) reported ADR data for women and men separately. Sex-stratified reporting of ADRs did not increase over the last decades. Six of the 11 studies did not report sex differences. Three studies reported a higher risk of angiotensin-converting enzyme inhibitor-related ADRs in women, 1 study showed higher digoxin-related mortality risk for women, and 1 study reported a higher risk of mineralocorticoid receptor antagonist-related ADRs in men. No sex differences in ADRs were reported for angiotensin II receptor blockers and β-blockers. Sex-stratified data were not available for ivabradine. CONCLUSIONS: These results underline the scarcity of ADR data stratified by sex. The study investigators call for a change in standard scientific practice toward reporting of ADR data for women and men separately.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".