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Record W7114987872 · doi:10.1093/pch/pxaf116.070

70 Learning to feed together: Standardizing preterm and late preterm infants’ transition from gavage to oral feeding

2025· article· en· W7114987872 on OpenAlexaffabout

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsMcGill University
Fundersnot available
KeywordsMilestoneTroubleshootingGestational ageWeight gainBirth weight

Abstract

fetched live from OpenAlex

Abstract Background The transition from gavage to oral feeding is a critical milestone in the care of preterm infants. Variability in the time needed to achieve successful oral feeding may be influenced not only by individual patient factors but also by inconsistencies in feeding practices. A lack of standardized feeding protocols can confuse parents and delay the feeding process, potentially extending the infant's hospital stay. Objectives We developed and implemented stage-based order sets to standardize the transition from gavage to full oral feeding in preterm infants admitted to the inpatient wards at the Montreal Children’s Hospital. Design/Methods Our multidisciplinary team conducted a comprehensive literature review to benchmark safe, effective, family- and baby-centered feeding progression practices. We divided the progression into four stages with defined criteria and developed corresponding order sets (Gavage only, Oral with gavage top-off, Active de-gavage, Ad lib on demand) and a guide to support stage selection. A feeding progression troubleshooting algorithm was also developed. Pre-implementation data was collected over six months in 2021. In addition to patient characteristics, key metrics included: corrected gestational age (CGA) at first oral feeds, time between first to full oral feeds, time from ward transfer to full oral feeds, CGA at full oral feeds, time from full oral feeds to discharge, average daily weight gain on the inpatient wards, inpatient wards length of stay (LOS) and total LOS. Order sets were implemented in May 2024 and post-implementation data collection is ongoing. Results On average, 11 infants per month are transferred to the inpatient wards for feeding progression. Since the implementation of the order sets, 39 infants have been transferred. Order sets were used in 56% of these cases. Among the infants, 64% were born preterm (gestational age <37 weeks, with a mean gestational age of 34+2 weeks). The average time from transfer to full oral feeding was 6 days, and from full oral feeding to discharge was 3.5 days, compared to 5.5 days and 6 days pre-implementation, respectively. The average CGA at full oral feeding remained consistent pre- and post-implementation at 38+2 weeks. The average ward LOS decreased from 13 days pre-implementation to 9 days post-implementation. Further statistical analysis will be conducted to assess the significance of the pre and post-implementation metrics. Conclusion A substantial number of preterm infants are transferred to inpatient wards for feeding progression, contributing to our high bed occupancy rates. Implementing an effective and standardized approach to feeding progression may help alleviate the strain on paediatric institutions and reduce the stress experienced by these already vulnerable families.

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)0.041
metaresearch head score (Gemma)0.117
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.117
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0130.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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