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Record W2968846295 · doi:10.1038/s41390-019-0526-1

Assessment of long-term neurodevelopmental outcome following trials of medicinal products in newborn infants

2019· review· en· W2968846295 on OpenAlexfundno aff
Neil Marlow, Lex W. Doyle, Peter J. Anderson, Samantha Johnson, Varsha Bhatt‐Mehta, Giancarlo Natalucci, Brian A. Darlow, Jonathan M. Davis, M. Turner

Bibliographic record

VenuePediatric Research · 2019
Typereview
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersHealth CanadaU.S. Food and Drug AdministrationJohns Hopkins UniversityUniversity College London Hospitals NHS Foundation TrustNational Institute for Health and Care ResearchBC Children's Hospital
KeywordsOutcome (game theory)Clinical trialMedicineProduct (mathematics)Expert opinionMultidisciplinary approachIntensive care medicineKey (lock)Duration (music)Computer sciencePolitical scienceEconomics

Abstract

fetched live from OpenAlex

There is significant uncertainty over the role of assessment of long-term neurodevelopmental outcome (LTO) in neonatal clinical trials. A multidisciplinary working group was established to identify key issues in this area and to make recommendations about optimal approaches to evaluate LTO in therapeutic trials in newborns, which can be developed by sponsors and investigators with other key stakeholders. A key consideration for neonatal trials is the potential for the investigational product to cause widespread effects and drives the need to assess outcome in multiple organs. Thus investigators must assess whether the product has an impact on the brain and the potential for it to cause potential effects on LTO. Critically, is assessment of LTO an important direct therapeutic target or a safety outcome? Such decisions and outcomes need to be specific to the product being studied and use published data, only considering expert opinion when prior evidence does not exist. In designing the trial, the balance of benefits, costs, and burdens of assessments to the researcher and families need to be considered. Families and parent advocates should be involved in design and execution of the study. A framework is presented for use by all key stakeholders to determine the need, nature, and duration of LTO assessments in regulatory trials involving newborn infants.

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)0.017
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.363
GPT teacher head0.537
Teacher spread0.174 · 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.

Study designObservational
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

Citations37
Published2019
Admission routes1
Has abstractyes

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