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Record W2921667293 · doi:10.14740/jocmr3797

Nutritional Support of Very Low Birth Weight Infants in a Tertiary Center in a Developing Country

2019· article· en· W2921667293 on OpenAlexvenueno aff
Manar Al‐lawama, Haneen Abu Alrous, Haitham Alkhatib, Abdelkareem Alrafaeh, Zaid Wakileh, Bushra Alawaisheh, Aseel Saadeh, Jumana Sharab, Eman Badran, Abla Albsoul‐Younes

Bibliographic record

VenueJournal of Clinical Medicine Research · 2019
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsnot available
FundersDeanship of Scientific Research, University of JordanUniversity of Jordan
KeywordsMedicineLow birth weightParenteral nutritionBirth weightNecrotizing enterocolitisGestational agePediatricsEnteral administrationObstetricsIntensive care medicinePregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: Very low birth weight infants (VLBWIs) are at high risk for nutritional deficiency. Enteral feeding is usually challenged by increased risk of necrotizing enterocolitis (NEC). The nutritional needs of VLBWIs are usually dependent on parenteral nutrition during early postnatal life. This study aimed to evaluate the nutritional service of VLBWIs at Jordan University Hospital. METHODS: This was a prospective follow-up study of VLBWIs with birth weight ≤ 1,500 g. Data were extracted from medical charts and laboratory database. RESULTS: In total, 43 VLBWIs met our inclusion criteria; of them, 21% were extremely low birth weight infants (ELBWIs). The mean gestational age was 29 weeks, and the mean birth weight was 1,218 g. The mean age of starting feeds was 3 days. Mean full feed age is 2 weeks. The most common side effect of total parenteral nutrition (TPN) was hypertriglyceridemia (35%). CONCLUSIONS: Nutritional care of VLBWIs is well established in our center. Initiating fortification earlier and working to increase mother's own breast milk supply is vital to improve growth in low resource setting.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.109
GPT teacher head0.503
Teacher spread0.394 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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