Pharmacokinetic considerations in chronic kidney disease and patients requiring dialysis
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease (CKD) is the progressive decline in renal function over time. Patients with end-stage renal disease require renal replacement therapy such as hemodialysis to support life. Hemodialysis patients require several medications to treat a variety of comorbid conditions. Polypharmacy accompanied by alterations in the pharmacokinetics of medications places hemodialysis patients at increased risk of drug accumulation and adverse events. AREAS COVERED: We review alterations in the pharmacokinetics of drugs in hemodialysis patients. The major areas of pharmacokinetics, absorption, distribution, metabolism and excretion, are covered and, where appropriate, differences between dialysis patients and non-dialysis CKD patients are compared. In addition, we review the importance of drug dialyzability and its potential impact on drug efficacy. Finally, we describe important clinical examples demonstrating nonrenal drug clearance is significantly altered in CKD. EXPERT OPINION: Decreases in renal drug excretion experienced by hemodialysis patients have been known for years. Recent animal and human clinical pharmacokinetic studies have highlighted that nonrenal clearance of drugs is also substantially decreased in CKD. Clinical pharmacokinetic studies are required to determine the optimal dosage of drugs in CKD and hemodialysis patients in order to decrease the incidence of adverse medication events in these patient populations.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".