Pharmacokinetic pharmacodynamic modeling of analgesics and sedatives in children
Bibliographic record
Abstract
Pharmacokinetic pharmacodynamic modeling is an important tool which uses statistical methodology to provide a better understanding of the relationship between concentration and effect of drugs such as analgesics and sedatives. Pharmacokinetic pharmacodynamic models also describe between-subject variability that allows identification of subgroups and dose adjustment for optimal pain management in individual patients. This approach is particularly useful in the pediatric population, where most drugs have received limited evaluation and dosing is extrapolated from adult practice. In children, the covariates of weight and age are used to describe size- and maturation-related changes in pharmacokinetics. It is important to consider both size and maturation in order to develop an accurate model and determine the optimal dose for different age groups. An adequate assessment of analgesic and sedative effect using pain scales or brain activity measures is essential to build reliable pharmacokinetic pharmacodynamic models. This is often challenging in children due to the multidimensional nature of pain and the limited sensitivity and specificity of some measurement tools. This review provides a summary of the pharmacokinetic and pharmacodynamic methodology used to describe the dose-concentration-effect relationship of analgesics and sedation in children, with a focus on the different pharmacodynamic endpoints and the challenges of pharmacodynamic modeling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".