Effect of Angiotensin-Converting Enzyme Inhibition on Functional Class in Patients with Left Ventricular Systolic Dysfunction—A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The effect of angiotensin converting enzyme (ACE) inhibitors on symptoms in patients with left ventricular systolic dysfunction (LVSD) is controversial. AIMS: To perform a meta-analysis of studies evaluating effect of ACE inhibitors on New York Heart Association (NYHA) class in patients with LVSD. METHODS: Individual data from 10389 patients in NYHA classes I-IV from four large long-term studies (2-4-year follow-up) and summary data from 2302 patients in NYHA classes II-IV from 16 short-term studies (3 months follow-up) were meta-analysed to assess changes in NYHA class. RESULTS: The large long-term studies showed a significant improvement in the worst NYHA classes (classes II-IV compared to class I) in the ACE inhibitor arm versus placebo, odds ratio (OR) = 0.875 (0.811-0.943) p = 0.0005. This effect was only present in studies which included patients with chronic heart failure and was particularly pronounced on deterioration to the worst NYHA class IV, OR = 0.66 (0.52-0.84) p = 0.001. There was no effect in the studies which included patients after myocardial infarction. The short-term chronic heart failure studies showed a significant improvement in NYHA class; OR for improvement of at least one NYHA class was 2.11 (1.48-2.98, 95% CI) p < 0.0001. CONCLUSION: ACE inhibition significantly improves symptomatic status measured as NYHA classification in patients with chronic heart failure.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.052 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".