Human and Bovine Colostrum for Prevention of Necrotizing Enterocolitis: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Human and bovine colostrum (HBC) administration has been linked to beneficial effects on morbidity and mortality associated with necrotizing enterocolitis (NEC). OBJECTIVES: To determine the effectiveness and safety of HBC for reducing NEC, mortality, sepsis, time to full-feed and feeding intolerance in preterm infants. DATA SOURCES: We conducted searches through Medline, Embase, Cumulative Index of Nursing and Allied Health Literature, Cochrane Central Register of Controlled Trials, and gray literature. STUDY SELECTION: Randomized controlled trials comparing human or bovine colostrum to placebo. DATA EXTRACTION: Two reviewers independently did screening, review, and extraction. RESULTS: Eight studies (385 infants) proved eligible. In comparison with placebo, HBC revealed no effect on the incidence of severe NEC (relative risk [RR]: 0.99; 95% confidence interval [CI] 0.48 to 2.02, I2 = 2.2%; moderate certainty of evidence), all-cause mortality (RR: 0.88; 95% CI 0.39 to 1.82, I2 = 0%; moderate certainty), culture-proven sepsis (RR: 0.78; 95% CI 0.53 to 1.14, I2 = 0%; moderate certainty), and feed intolerance (RR: 0.97; 95% CI 0.37 to 2.56, I2 = 55%; low certainty). HBC revealed a significant effect on reducing the mean days to reach full enteral feed (mean difference: −3.55; 95% CI 0.33 to 6.77, I2 = 41.1%; moderate certainty). The indirect comparison of bovine versus human colostrum revealed no difference in any outcome. LIMITATIONS: The number of patients was modest, whereas the number of NEC-related events was low. CONCLUSIONS: Bovine or human colostrum has no effect on severe NEC, mortality, culture-proven sepsis, feed intolerance, or length of stay. Additional research focused on the impact on enteral feeding may be needed to confirm the findings on this outcome.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".