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Record W4399070983 · doi:10.1038/s41390-024-03236-x

Translating regenerative medicine therapies in neonatal necrotizing enterocolitis

2024· article· en· W4399070983 on OpenAlexafffund
Niloofar Ganji, Brian T. Kalish, Martin Offringa, Bo Li, James W. Anderson, Sylvain Baruchel, Martin L. Blakely, Paolo De Coppi, Simon Eaton, Estelle Gauda, Nigel Hall, Anna Heath, Michael H. Livingston, Carol McNair, Robert D. Mitchell, Ketan Patel, Petros Pechlivanoglou, Hazel Pleasants-Terashita, Erin Pryor, Milica Radisic, Prakesh S Shah, Bernard Thébaud, Kasper Wang, Augusto Zani, Agostino Pierro

Bibliographic record

VenuePediatric Research · 2024
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsMount Sinai HospitalMcMaster UniversityMcMaster Children's HospitalChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesUniversity of TorontoSickKids FoundationHospital for Sick ChildrenPublic Health Ontario
FundersUniversity of Toronto
KeywordsNecrotizing enterocolitisMedicineIntensive care medicineRegenerative medicineInternal medicineBiologyStem cell

Abstract

fetched live from OpenAlex

Necrotizing enterocolitis (NEC) is characterized by intestinal inflammation and in severe cases, necrosis and perforation. It remains an unsolved clinical challenge with mortality rates up to 50%. 1 NEC survivors often develop early postoperative complications, short-gut syndrome, and neurodevelopmental disabilities. There are no specific medical therapies with clinical benefit in infants with NEC. Current NEC management involves the cessation of oral feeds, decompression of the stomach with a nasogastric tube, hemodynamic support with intravenous fluids and inotropes, and administration of broad-spectrum antibiotics for gut infections. 2 In severe disease, surgery is required to resect necrotic bowel. 2 The high mortality and morbidity of NEC indicate the need for innovative targeted treatments.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.111
GPT teacher head0.448
Teacher spread0.337 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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