Two‐stage vs mixed‐effect approach to pharmacodynamic modeling of propofol in children using state entropy
Bibliographic record
Abstract
OBJECTIVES: To compare the population pharmacodynamic (PD) models of propofol in children derived using two-stage and mixed-effect modeling approaches. METHODS: Fifty-two ASA 1 and 2 children aged 6-15 years presenting for gastrointestinal endoscopy were administered a loading dose of 4 mg·kg(-1) of propofol intravenously at an infusion rate determined by a randomization schedule. Using the plasma concentration predicted by the Paedfusor pharmacokinetic (PK) model, the propofol effect on state entropy (SE) was modeled using the two-stage and the mixed-effect modeling approaches, and the final population PD models were compared with each other in terms of their prediction performance, using median percentage and absolute percentage errors as well as mean absolute weighted error as metrics. The effects of age and body weight as prospective covariates were examined. RESULTS: The final population models were comparable with each other; the two-stage and the mixed-effect approaches resulted in a k(e0) of 2.38 and 2.66 min(-1), γ of 5.29 and 5.68, and EC(50) of 4.73 and 4.84 μg·ml(-1), respectively. The bootstrap estimates of the PD parameters were mean (SD) k(e0) = 2.38 (0.10), γ = 5.30 (0.30), and EC(50) = 4.73 (0.14). The PD parameters did not exhibit dependence on age and body weight. The parameters reported in this study in children were different from their adult counterparts reported in previous studies. CONCLUSIONS: Models derived using different mathematical approaches produced consistent model parameters. By virtue of its relative computational efficiency, the two-stage approach can serve as an attractive alternative to the mixed-effect approach in situations where data are not sparse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".