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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".