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Beneficial Effects of Probiotic Administration in Extremely Low Birthweight Infants: A Review

2016· review· en· W2475655936 on OpenAlexvenueno aff
Steffi Beinlich, John V. Logomarsino

Bibliographic record

VenueJournal of Nutritional Therapeutics · 2016
Typereview
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNecrotizing enterocolitisProbioticSepsisEnteral administrationPediatricsNeonatal intensive care unitPopulationLow birth weightBirth weightParenteral nutritionIntensive care medicineInternal medicinePregnancyEnvironmental healthBiology

Abstract

fetched live from OpenAlex

The aim of this review was to evaluate the beneficial effects of probiotic supplementation on extremely low birthweight infants (birthweight <1000 g). Extremely low birthweight (ELBW) infants are the most vulnerable population in the neonatal intensive care unit (NICU). They are at the highest risk for necrotizing enterocolitis (NEC), sepsis, and inadequate nutrition due to their immature gastrointestinal (GI) function. Nutrition plays an important role in the future neurodevelopmental outcomes of these infants. Research methods for the review were conducted using PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL). In total, eight research studies evaluated the effect of probiotic use in ELBW infants: three studies assessed GI colonization, five studies assessed enteral feeding and GI tolerance, one study assessed growth, five studies assessed NEC, five studies assessed sepsis, and two studies assessed length of hospital stay. This review found the use of probiotics improved GI tolerance, weight gain and length of hospital stay in ELBW infants, but was unable to make conclusions on the effect of probiotic use on incidences of NEC and sepsis. More research is needed in ELBW infants before making probiotic supplementation a standard of care in this population.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.659
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.373
Teacher spread0.323 · 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 designSystematic review
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

Citations0
Published2016
Admission routes1
Has abstractyes

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