Determinants of Successful Direct Breastfeeding at Hospital Discharge in High-Risk Premature Infants
Bibliographic record
Abstract
BACKGROUND: Despite a mother's intention to breastfeed, there are many barriers to feeding preterm infants that decrease breastfeeding rates. OBJECTIVE: The objective of this research was to determine factors associated with successful direct breastfeeding (DBF) of the preterm infant at hospital discharge. MATERIALS AND METHODS: A retrospective chart review of 69 preterm (<34 weeks' gestational age) infants in the neonatal intensive care unit, whose mothers intended to breastfeed, was conducted. Infant-, mother-, and feeding-related factors were examined by chi-square or t test for their relationship with breastfeeding success, and by multiple logistic regression to identify predictive factors. RESULTS: Successful DBF at discharge occurred in 64%. Mothers of infants who were breastfed were older (p < 0.0001); had less psychiatric illness (p = 0.03); and were less likely to smoke (p < 0.0001) and use recreational drugs (p = 0.04). The infants had higher birth weights (p = 0.03) and lower incidence of bronchopulmonary dysplasia (p = 0.04). A higher proportion of infants received DBF at their first oral feed (p < 0.001), and were discharged earlier (p = 0.03). Reduced milk supply was cited for breastfeeding failure in 36%. Older maternal age (odds ratio [OR] = 1.24, 95% confidence interval [CI] 1.02-1.51) and DBF at the first oral feed (OR = 7.72, 95% CI 1.37-43.6) were associated with successful DBF at discharge. CONCLUSION: Maternal age and method of first oral feed are critical predictors of breastfeeding success in preterm infants. Mothers should be encouraged to breastfeed at the infant's first oral attempt and strategic breastfeeding support should be provided before initiation of oral feeding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".