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Record W2797775985 · doi:10.1515/iss-2017-0050

Animal models of necrotizing enterocolitis: review of the literature and state of the art

2018· review· en· W2797775985 on OpenAlexaff
Adrienne Sulistyo, Abidur Rahman, George Biouss, Lina Antounians, Augusto Zani

Bibliographic record

VenueInnovative Surgical Sciences · 2018
Typereview
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsNecrotizing enterocolitisMedicineIntensive care medicineAnimal modelDiseaseEnterocolitisState of artPediatricsPathologyComputer scienceInternal medicineData science

Abstract

fetched live from OpenAlex

Necrotizing enterocolitis (NEC) remains the leading cause of gastrointestinal surgical emergency in preterm neonates. Over the last five decades, a variety of experimental models have been developed to study the pathophysiology of this disease and to test the effectiveness of novel therapeutic strategies. Experimental NEC is mainly modeled in neonatal rats, mice and piglets. In this review, we focus on these experimental models and discuss the major advantages and disadvantages of each. We also briefly discuss other models that are not as widely used but have contributed to our current knowledge of NEC.

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)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.683
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.002
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 head0.074
GPT teacher head0.389
Teacher spread0.315 · 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 designSystematic review
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

Citations25
Published2018
Admission routes1
Has abstractyes

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