Pharmacokinetics of drugs: newborn perspective
Bibliographic record
Abstract
Abstract: Safe and effective drug administration are pivotal goals of neonatal pharmacokinetics. Integrated knowledge of evolving pharmacokinetic principles, physiological characteristics, and maturational differences in term and preterm neonates is essential for effective, safe, and predictable drug response. Instances like ‘Grey baby syndrome’ chloramphenicol toxicity due to impaired glucuronidation and encephalopathy after hexachlorophene bath (to treat impetigo) due to increased transdermal absorption and impaired clearance have raised questions about our understanding of the complex interplay of factors in neonatal drug pharmacokinetics. This underscores the significance of knowledge and understanding of pharmacokinetic principles and the need for a population-specific approach. One must consider the complex relationship between multiple factors and differences among preterm neonates and young infants in terms of drug disposition before prescribing medications to the neonatal population. Consequently, clinical pharmacokinetics in neonates is as dynamic and diverse as the population. This review describes these dynamic changes leading to variable therapeutic efficacy or inadvertent exposures that can occur through the neonatal period. Therapeutic drug monitoring must be utilized to individualize the dosing of drugs in this vulnerable population whenever feasible. The objective of the review is to elucidate the principles of neonatal pharmacokinetics and the contribution of development, maturation, neonatal physiologic and pathologic states that govern neonatal pharmacokinetics so that drugs can be used efficaciously.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".