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Record W4407552281 · doi:10.1111/apa.70025

Neonatal Inflammation and Feeding Disorders at 1 Year in Infants With Congenital Gastrointestinal Malformations

2025· article· en· W4407552281 on OpenAlexaboutno aff
Cristina Mastropietro, Gaia Dimino, Nicolas Vinit, Frédérique Quetin, Véronique Rousseau, Victor Sartorius, Elsa Kermorvant‐Duchemin, Alexandre Lapillonne

Bibliographic record

VenueActa Paediatrica · 2025
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePediatricsConfoundingCohortCohort studyRetrospective cohort studyInternal medicine

Abstract

fetched live from OpenAlex

AIM: To investigate the associations between inflammatory markers and the risk of paediatric feeding disorders (PFD) at 1 year of age in infants with congenital gastrointestinal malformations (CGMs). METHODS: Neonates with CGMs admitted to our NICU and prospectively followed up in our outpatient clinic were included. The presence of PFD was assessed at the 1-year visit using the Montreal Children's Hospital Feeding Scale (MCH-FS). Data on potential risk factors for PFD were retrospectively collected. RESULTS: Fifty-nine neonates were included. They had a median MCH-FS at 1 year of 25 [IQR = 19-37]. PFD (MCH-FS > 45) was diagnosed in 15% of cases, of which 56% were severe. The number of days with a C-reactive protein (CRP) level > 40 mg/L was significantly higher in the PFD patients. After adjusting for confounding factors, a duration of CRP > 40 mg/L remained significantly associated with PFD at 1 year (OR = 1.23, [1.02-1.47]). Similarly, the number of neonatal surgical procedures (OR = 11.4, [2.15-60.6]) was independently associated with PFD at 1 year. CONCLUSION: PFD at 1 year was observed in 15% of newborns with CGMs in our cohort. Our results suggest that sustained severe inflammation caused by surgery and its complications during the neonatal period may have long-term effects on feeding behaviour.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.387

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)0.0000.000

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 head0.005
GPT teacher head0.234
Teacher spread0.229 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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