938
Bibliographic record
Abstract
Introduction: The purpose of paediatric pharmacokinetic studies is to provide precise estimates of the true value of pharmacokinetic parameters to inform dosing. Hypothesis: We evaluated age-groups studied and the precision of parameter estimates in peer-reviewed pharmacokinetic studies. Methods: Systematic reviews of 10 drugs included publications describing >1 pharmacokinetic study in patients <18 years. A pharmacokinetic study was defined as peer-reviewed primary data used to estimate one or more of the pharmacokinetic parameters: volume of distribution (Vd), clearance (CL), or half-life (HL). We defined uncertainty as the width of the 95% confidence interval divided by the study mean, and acceptable uncertainty as uncertainty <20%. We sought age-specific data for each of 5 pre-defined age groups for each drug. Results: Review of 6207 abstracts identified 114 eligible manuscripts containing 171 pharmacokinetic studies and 425 pharmacokinetic parameter estimates (145 Vd, 150 CL, 130 HL) and 364 uncertainty intervals. The median(IQR) sample size was 14(8-30). Estimates were derived from single age groups in 72(42.11%) studies however 28(56%) of the 50 drug/age-group pairs had no group-specific data. Acceptable uncertainty was found in 21(16.67%) Vd, 29(25.22%) HL, and 37(30.08%) CL estimates. Groups of potentially similar single-age studies were heterogeneous. Conclusions: Review of 10 commonly administered drugs found missing age-specific data was common. Available data were imprecise and not amenable to meta-analytic synthesis. Potential sources of variability included small sample sizes. Peer-reviewed pharmacokinetic data for children is incomplete, when available is imprecise, is likely to be a major source of dosing error and threat to the provision of optimal pharmacotherapy to children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.491 | 0.309 |
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".