Vancomycin Pharmacokinetics and Bayesian Estimation in Pediatric Patients
Bibliographic record
Abstract
The vancomycin pharmacokinetic profile was characterized in six pediatric patients and the potential of nonlinear mixed effects modeling and Bayesian forecasting for vancomycin monitoring was explored using NONMEM V (1.1). Based on steady state serial vancomycin concentrations, the estimates of mean t1/2, Vd, and Cl derived by the Sawchuk and Zaske method (1) were 3.52 hours, 0.57 L/kg, and 0.12 L/h per kg, respectively. NONMEM analysis demonstrated that a weight-adjusted two-compartment model described individual patients' data better than a comparable one-compartment model. The two-compartment estimates of mean t1/2alpha, t1/2beta, Vss, and Cl were 0.80 hour, 5.63 hours, 0.63 L/kg, and 0.11 L/h per kg, respectively. The relatively long mean t1/2alpha suggests that peak vancomycin concentrations measured earlier than 4 hours postdose do not reflect postdistributional serum concentrations. NONMEM population modeling revealed that a weight-adjusted two-compartment model provided a better fit than a comparable one-compartment model. The resulting population parameters and variances were fixed in NONMEM to obtain Bayesian predictions of individual vancomycin serum concentrations. Bayesian estimation with either a single midinterval or trough sample has the potential to provide accurate and precise predictions of vancomycin concentrations. This should be evaluated using a vancomycin population pharmacokinetic model based on a larger sample of pediatric patients.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".