Beta blockers in patients with end‐stage renal disease—Evidence‐based recommendations
Bibliographic record
Abstract
For patients who require hemodialysis, beta blockers offer a simultaneous opportunity and challenge in the treatment of cardiovascular disease. Beta blockers are well supported by data from nondialysis populations and directly mitigate the sympathetic overactivity that links chronic kidney disease with cardiovascular sequelae. However, the evidence supporting their use in patients receiving hemodialysis is sparse and the heterogeneity of the beta blocker class makes it difficult to prescribe these medications with confidence. Despite these limitations, both trial and observational data exist that can help guide the use of these medications. In this review, we outline the reasons to consider beta blockers for patients receiving hemodialysis, discuss the barriers to their use, and provide specific evidence-based recommendations for beta blocker use in patients with heart failure, hypertension, ischemic heart disease and arrhythmia.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".