DOSE-EXPOSURE SIMULATION FOR PIPERACILLIN-TAZOBACTAM DOSING STRATEGIES IN INFANTS AND YOUNG CHILDREN
Bibliographic record
Abstract
Background: Extended piperacillin-tazobactam (TZP) infusions have been associated with favorable outcomes. There are currently no pediatric dosing recommendations. Objective: To determine appropriate TZP dosing strategies in children 2 months – 6 years according to age and different minimal inhibitory concentrations (MICs). Methods: Age and weight were simulated for 1000 children. Post-hoc pharmacokinetic parameter estimates were generated using published clearance and volume of distribution data. For different dosing regimens, we estimated the probability of target attainment (PTA) over a range of MICs from 4 to 128 mg/L. The pharmacodynamic (PD) target was defined as free piperacillin concentrations above the MIC for ≥ 50% of the dosing interval. A PTA ≥ 90% was defined as optimal. Results: PTA decreased as MIC and age increased. In all age groups, standard dosing regimens (240-300 mg/kg/day, 0.5h infusions) failed to reach PTAs ≥ 90% at MICs ≥ 16 mg/L. Standard 0.5h infusions reached PTAs ≥ 90% at MICs up to 8 mg/L in infants > 2 to 6m. No 0.5h infusion reached PTAs ≥ 90% for MICs ≥ 4 mg/L in children > 6m. While none of the tested regimens were optimal at MICs > 16 mg/L in children > 6m, 100 mg/kg/dose every 6h as a 3h infusion reached PD target at MICs of 32 mg/L in infants > 2 to 6m. Conclusion: Up to MICs of 16 mg/L, 90 mg/kg/dose every 8h as a 2h infusion in infants > 2 to 6m and 100 mg/kg/dose every 8h as a 4h infusion in children > 6m-6y achieved PTAs ≥ 90%.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".