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Effect of Supplemental Donor Human Milk Compared With Preterm Formula on Neurodevelopment of Very Low Birth Weight Infants at 18 Months

2024· book-chapter· en· W4402000345 on OpenAlexaboutno aff
Nicholas D. Embleton

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsnot available
Fundersnot available
KeywordsLow birth weightMedicineObstetricsPediatricsPhysiologyBiologyPregnancy

Abstract

fetched live from OpenAlex

Abstract Donor human milk (DHM) is widely used as a supplement to a shortfall in mother’s own milk (MOM) supply, although preterm formula (PTF) is an alternative. This trial aimed to evaluate the impact of DHM compared with PTF on neurodevelopmental outcome. This pragmatic, double-blind trial conducted in 4 Canadian NICUs randomized 363 very low birth weight (VLBW) infants to receive either DHM (n = 181) or PTF (n = 182) when there was a shortfall in MOM supply. There were no differences in mean Bayley-III composite scores for cognitive, language, or motor development at 18 months corrected age. However, there was an important significant difference in necrotizing enterocolitis (NEC) stage ≥2 between the DHM versus the PTF group (1.7% v 6.6%; p = 0.02) in a preplanned exploratory analysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.275
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations0
Published2024
Admission routes1
Has abstractyes

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