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Record W2471049403 · doi:10.1111/bcp.13058

Design and conduct of early phase drug studies in children: challenges and opportunities

2016· review· en· W2471049403 on OpenAlexaff
Michael Rieder, Daniel B. Hawcutt

Bibliographic record

VenueBritish Journal of Clinical Pharmacology · 2016
Typereview
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsWestern University
Fundersnot available
KeywordsDrugStandardizationDosingMedicinePharmacologyClinical study designClinical trialDrug developmentPhase (matter)Intensive care medicineEngineering ethicsComputer scienceEngineeringPathology

Abstract

fetched live from OpenAlex

It has historically been very difficult to conduct early phase drug studies in children for a number of reasons related to ethics, acceptability, rarity, standardization, end points, safety, dosing and feasibility. Over the past decade there have been a number of developments including novel clinical trial design, in silico pharmacology and microdosing that have significantly enhanced the ability of investigators to conduct early phase drug studies in children. While the evolution of drug therapy is creating a series of new challenges, there has never been a better time for conducting drug studies in 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 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)0.050
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.702
GPT teacher head0.610
Teacher spread0.092 · 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.

Study designTheoretical or conceptual
DomainMethods
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

Citations29
Published2016
Admission routes1
Has abstractyes

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