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Record W2742743598 · doi:10.3390/children4080068

Key Challenges in the Search for Innovative Drug Treatments for Special Populations. Converging Needs in Neonatology, Pediatrics, and Medical Genetics

2017· article· en· W2742743598 on OpenAlexaff
Stuart MacLeod

Bibliographic record

VenueChildren · 2017
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsPharmacyMedicineAction (physics)NeonatologyKey (lock)Intensive care medicineMedical educationFamily medicineComputer scienceBiologyGenetics

Abstract

fetched live from OpenAlex

The explosion of knowledge concerning the interplay of genetic and environmental factors determining pathophysiology and guiding therapeutic choice has altered the landscape in pediatric clinical pharmacology and pharmacy. The need for innovative research methods and design expertise for small clinical trials to be undertaken in sparse populations has been accentuated. At the same time, shortfalls in critical human resources represent a key challenge, especially in low- and middle-income countries where the need for new research and education directions is greatest. Unless a specific action plan is urgently developed, there will be a continuing gap in availability of the essential expertise needed to address treatment challenges in special patient populations such as neonates, patients suffering from rare or neglected diseases, and children of all ages.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.164
GPT teacher head0.434
Teacher spread0.270 · 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 designObservational
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

Citations3
Published2017
Admission routes1
Has abstractyes

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