β-Blocker Dialyzability and Mortality in Older Patients Receiving Hemodialysis
Bibliographic record
Abstract
Some β-blockers are efficiently removed from the circulation by hemodialysis ("high dialyzability") whereas others are not ("low dialyzability"). This characteristic may influence the effectiveness of the β-blockers among patients receiving long-term hemodialysis. To determine whether new use of a high-dialyzability β-blocker compared with a low-dialyzability β-blocker associates with a higher rate of mortality in patients older than age 66 years receiving long-term hemodialysis, we conducted a propensity-matched population-based retrospective cohort study using the linked healthcare databases of Ontario, Canada. The high-dialyzability group (n=3294) included patients initiating atenolol, acebutolol, or metoprolol. The low-dialyzability group (n=3294) included patients initiating bisoprolol or propranolol. Initiation of a high- versus low-dialyzability β-blocker was associated with a higher risk of death in the following 180 days (relative risk, 1.4; 95% confidence interval, 1.1 to 1.8; P<0.01). Supporting this finding, we repeated the primary analysis in a cohort of patients not receiving hemodialysis and found no significant association between dialyzability and the risk of death (relative risk, 1.0; 95% confidence interval, 0.9 to 1.3; P=0.71). β-Blocker exposure was not randomly allocated in this study, so a causal relationship between dialyzability and mortality cannot be determined. However, our findings should raise awareness of this potentially important drug characteristic and prompt further study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".