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Knowledge‐driven approaches for the guidance of first‐in‐children dosing

2010· review· en· W1521727979 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePediatric Anesthesia · 2010
Typereview
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDosingMedicinePhysiologically based pharmacokinetic modellingPharmacokineticsIntensive care medicineAnticipation (artificial intelligence)DrugPharmacologyComputer scienceMachine learning

Abstract

fetched live from OpenAlex

Pediatric pharmacokinetic and pediatric safety and efficacy studies are, in most cases, a mandatory activity during the drug development process in North America and Europe. Pharmacokinetic modeling in anticipation of the pediatric clinical trial should take a data or knowledge-driven approach by employing either top-down or bottom-up approaches to assessing differential age-related dosing. These two approaches depend on different starting information and are likely to be used in conjunction with each other for the purposes of defining pediatric dosing guidelines. This review primarily focuses on the available bottom-up, mechanistic models for predicting age-dependent drug absorption, distribution and elimination, and their integration through the whole-body physiologically based pharmacokinetic (PBPK) model. The bottom-up approach incorporating adult and pediatric whole-body PBPK models for optimizing age-related dosing is detailed for a drug currently undergoing pediatric development.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.989
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.141
GPT teacher head0.388
Teacher spread0.247 · 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