β-Blocker Dialyzability in Maintenance Hemodialysis Patients
Bibliographic record
Abstract
Background and objectives There is a paucity of data available to describe drug dialyzability. Of the available information, most was obtained before implementation of modern hemodialysis membranes. Our study characterized dialyzability of the most commonly prescribed β -blockers in patients undergoing high-flux hemodialysis. Design, setting, participants, & measurements Patients on hemodialysis ( n =8) were recruited to an open label, pharmacokinetic, four-way crossover trial. Single doses of atenolol, metoprolol, bisoprolol, and carvedilol were administered on separate days in random order to each patient. Plasma and dialysate drug concentrations were measured, and dialyzability was determined by the recovery clearance and arterial venous difference methods. Results Using the recovery clearance method, the dialytic clearance values for atenolol, metoprolol, bisoprolol, and carvedilol were 72, 87, 44, and 0.2 ml/min, respectively ( P <0.001). Applying the arterial venous difference method, the dialytic clearance values of atenolol, metoprolol, bisoprolol, and carvedilol were 167, 114, 96, and 24 ml/min, respectively ( P <0.001). Conclusions Atenolol and metoprolol are extensively cleared by hemodialysis compared with the negligible dialytic clearance of carvedilol. Contrary to estimates of dialyzability on the basis of previous literature, our data indicate that bisoprolol is also dialyzable. This finding highlights the importance of conducting dialyzability studies to definitively characterize drug dialytic clearance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".