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Record W2403569178 · doi:10.1177/0148607116629676

Human Milk for Ill and Medically Compromised Infants

2016· article· en· W2403569178 on OpenAlexafffund
Sara DiLauro, Sharon Unger, Debbie Stone, Deborah L. O’Connor

Bibliographic record

VenueJournal of Parenteral and Enteral Nutrition · 2016
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsMount Sinai HospitalHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineBreast milkIntensive care medicineChylothoraxParenteral nutritionInfant formulaPediatricsSurgeryBiology

Abstract

fetched live from OpenAlex

The use of human milk (mother's own milk and/or donor milk) in ill or medically compromised infants frequently requires some adaptation to address medical diagnoses and/or altered nutrition requirements. This tutorial describes the nutrition and immunological benefits of breast milk as well as provides evidence for the use of donor milk when mother's own milk is unavailable. Several strategies used to modify human milk to meet the medical and nutrition needs of an ill or medically compromised infant are reviewed. These strategies include (1) the standard fortification of human milk to support adequate growth, (2) the novel concept of target fortification in preterm infants, (3) instructions on how to alter maternal diet to address cow's milk protein intolerance and/or allergy in breast milk-fed infants, and (4) the removal and modification of the fat in breast milk used in infants diagnosed with chylothorax.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.312
Teacher spread0.290 · 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 designNot applicable
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

Citations13
Published2016
Admission routes2
Has abstractyes

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