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Necrotizing Enterocolitis Associated with Congenital Heart Disease—A Review Article

2022· article· en· W4220965862 on OpenAlexaff
Akhil Maheshwari, Sriya Roychaudhuri, Gurpreet Grewal, Sakethram Saravu Vijayashankar, Pascal M. Lavoie

Bibliographic record

VenueNewborn · 2022
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsBC Children's HospitalB.C. Women's Hospital & Health Centre
FundersNational Heart, Lung, and Blood Institute
KeywordsNecrotizing enterocolitisMedicineHeart diseasePediatricsIntensive care medicineDiseaseCohortPopulationCausality (physics)Cohort studyNeonatal intensive care unitInternal medicine

Abstract

fetched live from OpenAlex

Necrotizing enterocolitis (NEC) is a relatively rare but devastating entity associated classically with the preterm cohort in the neonatal intensive care unit. Preterm and term babies with congenital heart disease are at risk of a number of comorbidities because of the hemodynamic derangements due to a structurally abnormal heart and the corrective procedures adopted. Necrotizing enterocolitis is one of the dreaded complications associated with this cohort and impacts the course of these babies in the hospital in a major way. A large majority of term babies with NEC are in the backdrop of a significant congenital cardiac lesion. This review article summarizes the literature and elaborates this entity including its specific features, risk factors associated with its causality, histopathology and related aspects of hemodynamics, and feeding in this vulnerable population. It also provides insight into modifiable risk factors and early markers of detection of gut necrosis to facilitate prevention and early detection. It highlights the subtle but definite difference in outcome variables to help physicians enable the parents of babies with heart disease to develop a better understanding of the entity and its expected course while counseling.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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
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

Citations14
Published2022
Admission routes1
Has abstractyes

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