Pharmacokinetic characterization of baclofen in patients with chronic kidney disease: dose adjustment recommendations
Bibliographic record
Abstract
The pharmacokinetics of baclofen is well delineated in subjects with normal kidney function (KF); however, pharmacokinetics data in patients with chronic kidney disease (CKD) are not and dosage recommendations remain empirical. The effects of CKD on baclofen pharmacokinetics were assessed through a multi-center, open-label, single 5-mg dose, pharmacokinetics study. The KF was measured as the creatinine clearance (CrCL) calculated with the Cockroft-Gault (C-G) equation or as the estimated glomerular filtration rate (eGFR) using subjects' CKD-EPI equation. Subjects were assigned to 1 of 4 groups based on their CrCL (>80 mL/min, 50-80 mL/min; 30-50 mL/min and <30 mL/min). Cmax was not statistically different between the groups, while AUC and T1/2el increased, and CL/F decreased, with increasing severity of CKD. Baclofen's oral clearance and CrCL were statistically significantly correlated, and the trend was the same when classifying subjects either with the CKD-EPI or C-G equations. Linear equations using KF as variable were set to recommend individual dose reduction in CKD patients. Results suggest a mean dose reduction of 1/3, 1/2, and 2/3 in patients with mild, moderate, and severe CKD respectively, in order to achieve baclofen exposure comparable to that observed in healthy subjects.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".