Adverse Drug Reactions to Guideline-Recommended Heart Failure Drugs in Women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.011 |
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 it