Benefits of a Standardized Enteral Feeding Protocol on the Nutrition and Health Outcomes of Very Low Birth Weight Preterm Infants
Bibliographic record
Abstract
Purpose: To compare nutrition and health outcomes before and after implementing a standardized enteral feeding protocol on nutrition and health outcomes in very low birth weight preterm infants. Methods: A retrospective chart review was performed evaluating preterm infants, born less than 34 weeks gestation and weighing less than 1500 g, before and after the implementation of a standardized enteral feeding protocol. Outcomes included weaning of parenteral nutrition, initiation and advancement of enteral feeds, initiation of human-milk fortifier (HMF), change in weight z-score and neonatal morbidities. Results: Fifty-six infants (30 in pre-group, 26 in post-group) met the inclusion criteria. Infants in the standardized enteral feeding protocol group started enteral feeds earlier (p = 0.039) and received full HMF fortification at lower weights (p = 0.033) than those in the pre-group. Fewer days on continuous positive airway pressure (p = 0.021) and lower rates of bronchopulmonary dysplasia (p = 0.018) were also observed in the post-group. Weaning of parenteral nutrition and weight z-score were not significantly different between groups. There were no differences in other morbidities. Conclusion: Study results suggest that adopting a standardized enteral feeding protocol may promote early initiation of enteral feeds and fortification.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".