Angiotensin-Converting Enzyme Inhibitors and Survival in Women and Men with Heart Failure
Bibliographic record
Abstract
BACKGROUND: Several randomized controlled trials demonstrate that angiotensin-converting enzyme (ACE) inhibitors improve survival in patients with congestive heart failure (CHF). However, whether ACE inhibitors benefit both sexes is not adequately addressed. PURPOSE: Our objective was to determine the effectiveness of ACE inhibitors in women with CHF. METHODS: The Quebec hospital discharge database was linked with the physician and drug claims database to identify a cohort with a discharge diagnosis of CHF between January 1998 and March 2003. In this retrospective cohort study, subjects who filled a prescription for ACE inhibitors (19,220 exposed) were compared to those who never filled such prescription (8617 non-exposed). The primary outcome was survival by exposure to ACE inhibitors. MAIN FINDINGS: There were 14,693 women (67% exposed) and 13,144 men (72% exposed). The 1 year mortality was 19.5% and 30% in those exposed and non-exposed, respectively. A significant survival benefit was demonstrated in both sexes exposed to ACE inhibitors [adjusted hazard ratio (95% confidence interval): women 0.80 (0.76-0.85); men 0.71 (0.67-0.75)]. PRINCIPAL CONCLUSIONS: ACE inhibitors improve survival in both sexes with CHF, but the protective effect appears to be greater in men. Our results support the current recommendations for the management of women with CHF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".