How to Optimize Drug Delivery in Renal Replacement Therapy
Bibliographic record
Abstract
Drug dosing in the setting of acute kidney injury (AKI) is complicated by several factors such as pharmacokinetic changes in renal failure, inaccuracy of renal estimating equations in this setting, lack of therapeutic drug monitoring capability for most drugs, and use of extracorporeal renal replacement. Pharmacokinetic changes include decreases in protein binding and drug metabolism. Renal estimating equations most often overestimate renal clearance in AKI. Additionally, it is well recognized that some drugs are significantly cleared by extracorporeal therapy. Patients with AKI are therefore at risk for adverse outcomes of drug therapy. It has been reported that approximately half of patients with reduced renal clearance receive drug doses that are 2.5 times higher than the recommended maximum dose. To ensure efficacy and prevent toxicity, therapeutic drug monitoring is highly recommended. However, in the absence of drug monitoring, adequate concentrations can only be inferred from clinical response. A clinician must weigh the risks and benefits of possible over-dosing or under-dosing based on the therapeutic index of the drug and the clinical situation. This article will review the important factors to consider for drug dosing in patients with AKI receiving continuous renal replacement therapy and sustained low-efficiency dialysis.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".