Pharmacokinetics of Cefepime in Children on Extracorporeal Membrane Oxygenation
Bibliographic record
Abstract
Background: Cefepime is a first-line therapy for Gram-negative infections in children on extracorporeal membrane oxygenation. Cefepime pharmacokinetics (PK) in children on extracorporeal membrane oxygenation still needs to be better established. Methods: This was a prospective single-center PK study. A maximum of 12 PK samples per patient were collected in children <18 years old on extracorporeal membrane oxygenation who received clinically indicated cefepime. External validation of a previously published population PK model was performed by applying the model in a new data set. The predictive performance of the model was determined by calculating prediction errors. Because of poor predictive performance, a revised model was developed using NONMEM and a combined data set that included data from both studies. Dose-exposure simulations were performed using the final model. Optimal dosing was judged based on the ability to maintain free cefepime concentrations above the minimal inhibitory concentration (MIC) for 68% and 100% of the dosing interval. Results: Seventeen children contributed 105 PK samples. The mean (95% CI) and median (interquartile range) prediction errors were 33.7% (19.8–47.7) and 17.5% (−22.6 to 74.4). A combined data set was created, which included 33 children contributing 310 PK samples. The final improved 2-compartment model included weight and serum creatinine on clearance and oxygenator day and blood transfusion on volume of the central compartment. At an MIC of 8 mg/L, 50 mg/kg/dose every 8 hours reached target concentrations. Conclusions: Dosing intervals of 8 hours were needed to reach adequate concentrations at an MIC of 8 mg/L. Longer dosing intervals were adequate with higher serum creatinine and lower MICs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".