Predictive Performance of a Busulfan Pharmacokinetic Model in Children and Young Adults
Bibliographic record
Abstract
BACKGROUND: Recently a pediatric pharmacokinetic (PK) model was developed for busulfan to explain the wide variability in PK of busulfan in children, as this variability is known to influence the outcome of hematopoietic stem cell transplantation in terms of toxicity and event free survival. This study assesses the predictive performance of this busulfan PK model in a new, more diverse pediatric population, including data from patients with different underlying diseases, ethnicities, body weights, ages, and body mass indices, from 5 international pediatric transplant centers. PATIENTS AND METHODS: The previously published (original) busulfan PK model was developed from data of 245 patients (0.1-26 years of age). To externally validate this model, data were collected from another 158 patients (0.1-35 years) who underwent hematopoietic stem cell transplantation in 5 international transplant centers. Observed versus predicted plots, normalized prediction distribution error analysis, refit of the model on the external (n = 158) and combined datasets (n = 403), and subpopulation analyses were evaluated. RESULTS: The original busulfan PK model was found to be stable and parameter estimates precise. Concentrations predicted by this model were in good agreement with the observed concentrations from the 5 external datasets. Plasma concentrations in patients with different underlying diseases, ethnicities, body weights, ages, and body mass indices were adequately predicted. CONCLUSIONS: Our pediatric busulfan PK model has been externally validated. This model predicts busulfan concentrations in pediatric and young adult patients ranging between 3 and 86 kg without bias and with good precision, regardless of transplant center, underlying disease, ethnicity, body weight age, or body mass index. This busulfan PK model forms the basis for individualized busulfan dosing.
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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 | 0.005 |
| 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 | 0.000 | 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".