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Paediatric Pharmacotherapy and Drug Regulation -- Moving Past the Therapeutic Orphan

2020· preprint· en· W3083899660 on OpenAlexaff
Charlotte Moore Hepburn, Michael Rieder

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsEuropean unionOrphan drugNegotiationMedicineDrugClinical trialGovernment (linguistics)PharmacotherapyClinical PracticeHealth careOff-label useDrug developmentPolitical scienceFamily medicineInternational tradeBusinessPharmacologyPsychiatryLawBioinformatics

Abstract

fetched live from OpenAlex

The development of specific drug therapy for children was a paradigm changing event that transformed paediatric medical practice. However a series of tragedies involving drug treatment for children resulted in a gap developing between drug regulation and practice, with the majority of drugs used in child health care being used “off label” rendering children therapeutic orphans. Over the past two decades changes in drug regulation led by the US FDA and followed by the European Union’s EMA have led to substantial changes in how new drugs with potential use in children are studied and labelled. While these changes have substantially improved labeling for new drugs, there has been much less progress with older drugs. As well while the unique challenges of conducting clinical research in children have been addressed by novel clinical trial designs, many of these innovations have not been translated into approaches accepted for the drug approval process. The regulations applying to the need for paediatric studies currently are only applicable in the United States and the European Union, and there is less impetus for paediatric labeling in other jurisdictions. This impacts on a number of issues beyond labeling, including the availability of child-friendly formulations. Finally the impact of Brexit on paediatric drug studies in the UK remains unclear and subject to on-going negotiations between the UK government and the European Union.

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 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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.070
GPT teacher head0.370
Teacher spread0.300 · 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 teacher head, not a consensus.

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

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

Citations0
Published2020
Admission routes1
Has abstractyes

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