β-Blocker Use and Cardiovascular Outcomes in Hemodialysis: A Systematic Review
Bibliographic record
Abstract
Rationale & Objective: There is conflicting evidence regarding the type of β-blockers to use in dialysis patients. This systematic review seeks to determine whether highly dialyzable β-blockers are associated with higher rates of cardiovascular events and mortality in hemodialysis patients than poorly dialyzable β-blockers. Study Design: A systematic review of the existing literature was conducted. A meta-analysis was performed using data from the selected studies. Setting & Study Populations: Participants were from the United States, Canada, and Taiwan. The mean ages of participants ranged from 55.9-75.7 years. Selection Criteria for Studies: We searched the Ovid MEDLINE database from 1990 to September 2020. Studies without adult hemodialysis participants and without comparisons of at least 2 β-blockers of different dialyzability were excluded. Data Extraction: Baseline and adjusted outcome data were extracted from each study. Analytical Approach: Random-effects models were used to calculate pooled risk ratios using fully adjusted models from individual studies. Results: = 0.35). Only 1 study reported on adverse events. Intradialytic hypotension was more common in those on carvedilol (a poorly dialyzable β-blocker) compared with those on metoprolol (a highly dialyzable β-blocker; adjusted incidence rate ratio, 1.10; 95% CI, 1.09-1.11). Limitations: No randomized controlled trials were identified. Each study used different analytic methods and different definitions for outcomes. Classifications of β-blockers varied. Only 1 study reported on adverse events. Conclusions: Pooled data suggest highly dialyzable β-blockers are associated with similar mortality events and fewer cardiovascular events compared with poorly dialyzable β-blockers.
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.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".