Pharmacokinetic and pharmacogenetic determinants and considerations in chemotherapy selection and dosing in infants
Bibliographic record
Abstract
INTRODUCTION: There is a lack of high-quality data regarding optimal chemotherapy dosage regimens among infants. Dosing regimens for chemotherapy during the first year of life are commonly based on empiric recommendations extrapolated from older children; however, balancing efficacy and toxicity is critical as severe adverse drug reactions may lead to treatment failure or reduced adherence to needed medications. AREAS COVERED: This review describes pharmacokinetic and pharmacogenetic considerations when administering chemotherapeutic agents to infants. Examples of commonly used agents are provided with practical recommendations for dosing adjustments. EXPERT OPINION: Optimal chemotherapy for children and infants in particular has lagged behind the remarkable progress in cancer treatment and it is clear that far more basic and clinical research are needed with respect to the mechanistic basis of age-dependent differences in pharmacokinetic parameters. More recent studies which have combined pharmacokinetic data with clinical toxicity and outcome data have resulted in a number of more evidence-based guidelines at least for the initial chemotherapy dosing; however, at present, the dosing of chemotherapy drugs in neonates and infants remains largely empiric.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".