A simulation study to assess the influence of population pharmacokinetic model selection on initial dosing recommendations of vancomycin in neonates
Bibliographic record
Abstract
Aims The accuracy of model‐informed precision dosing largely depends on selecting the most appropriate population pharmacokinetic (popPK) model from many available options. This study aims to evaluate the concordance of optimal initial simulated doses among various vancomycin popPK models developed in neonates and to explore the role of predictive performance in explaining the variability in probability of target attainment (PTA). Methods A virtual neonatal patient population was created and 26 previously externally evaluated vancomycin popPK models were used to simulate 5 different dosing regimens. For each simulated scenario, the area under the concentration–time curve and PTA were calculated to assess the agreement on optimal initial doses across the 26 models. A multiple regression was performed to explore the impact of the models' predictive performance on PTA. Results For most models (15/26), there was an agreement on the optimal dosing regimen. The highest PTA being achieved by the model with the best a priori predictive performance. The multiple regression model significantly predicted mean ln‐transformed PTA, with F(2, 23) = 5.406 and P = .010, yielding an adjusted R2 of .21. PTA was significantly influenced by imprecision (P = .048) but not bias (P = .469). Conclusion In conclusion, our study demonstrated that, despite the variability in bias and imprecision, there was a consensus on the initial optimal doses for the majority of models; however, models with superior a priori predictive performance yielded higher PTA values. Bias and imprecision alone only seem to predict a small proportion of the variability in PTA, with imprecision having a more pronounced effect.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".