Determination of Vancomycin Pharmacokinetics in Neonates To Develop Practical Initial Dosing Recommendations
Bibliographic record
Abstract
Variability in neonatal vancomycin pharmacokinetics and the lack of consensus for optimal trough concentrations in neonatal intensive care units pose challenges to dosing vancomycin in neonates. Our objective was to determine vancomycin pharmacokinetics in neonates and evaluate dosing regimens to identify whether practical initial recommendations that targeted trough concentrations most commonly used in neonatal intensive care units could be determined. Fifty neonates who received vancomycin with at least one set of steady-state levels were evaluated retrospectively. Mean pharmacokinetic values were determined using first-order pharmacokinetic equations, and Monte Carlo simulation was used to evaluate initial dosing recommendations for target trough concentrations of 15 to 20 mg/liter, 5 to 20 mg/liter, and ≤20 mg/liter. Monte Carlo simulation revealed that dosing by mg/kg of body weight was optimal where intermittent dosing of 9 to 12 mg/kg intravenously (i.v.) every 8 h (q8h) had the highest probability of attaining a target trough concentration of 15 to 20 mg/liter. However, continuous infusion with a loading dose of 10 mg/kg followed by 25 to 30 mg/kg per day infused over 24 h had the best overall probability of target attainment. Initial intermittent dosing of 9 to 15 mg/kg i.v. q12h was optimal for target trough concentrations of 5 to 20 mg/liter and ≤20 mg/liter. In conclusion, we determined that the practical initial vancomycin dose of 10 mg/kg vancomycin i.v. q12h was optimal for vancomycin trough concentrations of either 5 to 20 mg/liter or ≤20 mg/liter and that the same initial dose q8h was optimal for target trough concentrations of 15 to 20 mg/liter. However, due to large interpatient vancomycin pharmacokinetic variability in neonates, monitoring of serum concentrations is recommended when trough concentrations between 15 and 20 mg/liter or 5 and 20 mg/liter are desired.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".