The Safety and Efficacy of Mineralocorticoid Receptor Antagonists in Patients Who Require Dialysis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patients who require dialysis are at high risk for cardiovascular mortality, which may be improved by mineralocorticoid receptor antagonists (MRAs). STUDY DESIGN: Systematic review and meta-analysis of randomized controlled trials. SETTING & POPULATION: Adults undergoing long-term hemodialysis or peritoneal dialysis with or without heart failure. SELECTION CRITERIA FOR STUDIES: Randomized controlled trials evaluating an MRA in dialysis and reported at least one outcome of interest. INTERVENTION: Spironolactone (8 trials) or eplerenone (1 trial) compared to placebo (7 trials) or standard of care (2 trials). OUTCOMES: Cardiovascular and all-cause mortality, hyperkalemia, serum potassium level, hypotension, change in blood pressure, and gynecomastia. RESULTS: We identified 9 trials including 829 patients. The overall quality of evidence was low due to methodologic limitations in most of the included trials. The relative risk (RR) for cardiovascular mortality was 0.34 (95% CI, 0.15-0.75) for MRA-treated compared with control patients. The RR for all-cause mortality was 0.40 (95% CI, 0.23-0.69). The RR for hyperkalemia for MRA treatment was 3.05 (95% CI, 1.21-7.70). Sensitivity analyses demonstrated wide variability in RRs for cardiovascular mortality, all-cause mortality, and hyperkalemia, suggesting further uncertainty in the confidence of the primary results. LIMITATIONS: Trial quality and size insufficient to robustly and precisely identify a treatment effect. CONCLUSIONS: Given the uncertainty of both the benefits and harms of MRAs in dialysis, large high-quality trials are required.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.004 | 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".