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Record W2895535184 · doi:10.3390/nu10101433

Evaluation of A Concentrated Preterm Formula as a Liquid Human Milk Fortifier in Preterm Babies at Increased Risk of Feed Intolerance

2018· article· en· W2895535184 on OpenAlexafffund
Anish Pillai, Susan Albersheim, Julie Matheson, Vikki Lalari, Sylvia Wei, Sheila M. Innis, Rajavel Elango

Bibliographic record

VenueNutrients · 2018
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Institutes of HealthAbbott Canada
KeywordsMedicineNecrotizing enterocolitisInfant formulaSepsisPediatricsParenteral nutritionIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

There are concerns around safety and tolerance of powder human milk fortifiers to optimize nutrition in preterm infants. The purpose of this study was to evaluate the tolerance and safety of a concentrated preterm formula (CPF) as a liquid human milk fortifier (HMF) for premature infants at increased risk of feeding intolerance. We prospectively enrolled preterm infants over an 18-month period, for whom a clinical decision had been made to add CPF to human milk due to concerns regarding tolerance of powder HMF. Data on feed tolerance, anthropometry, and serum biochemistry values were recorded. Serious adverse events, such as mortality, necrotizing enterocolitis (NEC), and sepsis, were monitored. A total of 29 babies received CPF fortified milk during the study period. The most common indication for starting CPF was previous intolerance to powder HMF. Feeding intolerance was noted in 4 infants on CPF. The growth velocity of infants was satisfactory (15.9 g/kg/day) after addition of CPF to feeds. The use of CPF as a fortifier in preterm babies considered at increased risk for feed intolerance seems well tolerated and facilitates adequate growth. Under close nutrition monitoring, this provides an additional option for human milk fortification in this challenging subgroup of preterm babies, especially in settings with limited human milk fortifier options.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.341
Teacher spread0.308 · 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 designBench or experimental
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

Citations17
Published2018
Admission routes2
Has abstractyes

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