Machines that help machines to help patients: optimising antimicrobial dosing in patients receiving extracorporeal membrane oxygenation and renal replacement therapy using dosing software
Bibliographic record
Abstract
Intensive care unit (ICU) patients with end-organ failure will require specialised machines or extracorporeal therapies to support the failing organs that would otherwise lead to death. ICU patients with severe acute kidney injury may require renal replacement therapy (RRT) to remove fluid and wastes from the body, and patients with severe cardiorespiratory failure will require extracorporeal membrane oxygenation (ECMO) to maintain adequate oxygen delivery whilst the underlying pathology is evaluated and managed. The presence of ECMO and RRT machines can further augment the existing pharmacokinetic (PK) alterations during critical illness. Significant changes in the apparent volume of distribution (V d ) and drug clearance (CL) for many important drugs have been reported during ECMO and RRT. Conventional antimicrobial dosing regimens rarely consider the impact of these changes and consequently, are unlikely to achieve effective antimicrobial exposures in critically ill patients receiving ECMO and/or RRT. Therefore, an in-depth understanding on potential PK changes during ECMO and/or RRT is required to inform antimicrobial dosing strategies in patients receiving ECMO and/or RRT. In this narrative review, we aim to discuss the potential impact of ECMO and RRT on the PK of antimicrobials and antimicrobial dosing requirements whilst receiving these extracorporeal therapies. The potential benefits of therapeutic drug monitoring (TDM) and dosing software to facilitate antimicrobial therapy for critically ill patients receiving ECMO and/or RRT are also reviewed and highlighted.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".