MétaCan
Menu
Back to cohort
Record W2893858554 · doi:10.1002/jcph.1316

Pharmacometric Modeling and Simulation Is Essential to Pediatric Clinical Pharmacology

2018· review· en· W2893858554 on OpenAlexaff
Michael Neely, David S. Bayard, Amit Desai, Laura Kovanda, Andrea N. Edginton

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2018
Typereview
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of Waterloo
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institute of General Medical Sciences
KeywordsClinical pharmacologyMedicineSubspecialtyPharmacologyBroad spectrumSpecialtyDrug developmentIntensive care medicineDrugMedical physicsFamily medicine

Abstract

fetched live from OpenAlex

Pediatric clinical pharmacology now encompasses a wide range of activities, including drug pharmacokinetic and pharmacodynamic modeling and simulation, also known as pharmacometrics. Pediatric clinical pharmacologists may be physicians but are more likely to be pharmacists or PhD scientists, and pediatric clinical pharmacology today is largely a research specialty rather than a subspecialty for direct patient care. Pharmacometrics, including "top-down" population modeling and "bottom-up" physiologically based pharmacokinetic modeling, has become an indispensable tool for pharmaceutical industry scientists, government regulators, academic researchers, and even a handful of patient-oriented practitioners. This review summarizes the application of pharmacometrics within each of these domains and predicts future trends of further applications across the spectrum of pediatric clinical pharmacology from drug development to patient care.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.005
Science and technology studies0.0000.003
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.003

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.

Opus teacher head0.444
GPT teacher head0.648
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations17
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Clinical PharmacologySame topicPharmaceutical studies and practicesFrench-language works237,207