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Record W3131435632 · doi:10.3390/children8020139

The E-Nurture Project: A Hybrid Virtual Neonatal Follow Up Model for 2021

2021· review· en· W3131435632 on OpenAlexaff
Paige Church, Rudaina Banihani, Jo Watson, Wan Ting Nancy Chen, Marilyn Ballantyne, Elizabeth Asztalos

Bibliographic record

VenueChildren · 2021
Typereview
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsDeclarationNature versus nurturePandemicCoronavirus disease 2019 (COVID-19)Identification (biology)Computer scienceMedicineBiologyProgramming language

Abstract

fetched live from OpenAlex

Neonatal follow-up has long focused on a model of surveillance and identification of short-term outcomes. This model has long become outdated, with evidence documenting the need for longer follow-up with known school-based challenges and significant gaps in knowledge by educators. This article reviews the history of neonatal follow-up and demonstrates a novel approach to neonatal follow-up, built largely with a hybrid virtual platform, which then became essential with the declaration of the pandemic in 2020.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.034
GPT teacher head0.316
Teacher spread0.283 · 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 designNot applicable
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

Citations11
Published2021
Admission routes1
Has abstractyes

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