Expansion of a Pharmacokinetic Model for Diazepam to Characterize Real‐World IV and Oral Data in Children With and Without Obesity
Bibliographic record
Abstract
Diazepam is a benzodiazepine approved for use in adults and children. The label incorporates recommended dosing for status epilepticus in children. Published population pharmacokinetic (PK) modeling recommends an intravenous bolus dose of 0.2 mg/kg capped at 8 mg to reach the suggested target exposure of 200-600 ng/mL at 10 min post dose in children up to 17 years of age. This model was developed for children generally without obesity based on IV data, and it is unclear how increased body weight may affect exposure or target attainment given capped dosing. Diazepam concentrations after IV or oral administration for 61 children aged 2.5 to 20.6 years were used to externally evaluate the model including the addition of fixed oral absorption parameters. Then, PK parameters were re-estimated with the external population alone and again in combination with the original population. Re-estimated parameters from the combined population were used to simulate recommended dosing for children with and without obesity. The external dataset included 88 plasma concentrations from 61 children (54 with obesity) receiving diazepam per standard of care. The external evaluation resulted in 34.5% of predicted values within 30% of the observed concentration. Parameter re-estimation resulted in increased central volume of distribution (26% increase from a previous model), reduced peripheral volume of distribution and intercompartmental clearance, and similar clearance estimates. Simulations demonstrated that dosing caps may prevent children with obesity from reaching the suggested target exposure that is recommended for the treatment of status epilepticus. Further study is needed to evaluate the target exposure range in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".