Lactoferrin Supplementation to Prevent Late-Onset Sepsis in Preterm Infants: A Meta-Analysis
Bibliographic record
Abstract
Abstract Objective This study aimed to systematically review and meta-analyze the role of lactoferrin supplementation to prevent late-onset sepsis (LOS) in preterm infants. Study Design Database search include PubMed, Web of Science, and Cochrane central for randomized clinical trial (RCTs). The Cochrane Grading of Recommendations Assessment, Development, and Evaluation methodology was used for summarizing the results. Results Ten RCTs involving 3,679 infants were included. Lactoferrin supplementation with or without probiotics decreased all LOS (relative risk [RR]: 0.56; 95% confidence interval [CI]: 0.36–0.86; I 2 = 58%; 10 studies; 3,470 subjects; level of evidence [LOE]: low) significantly. Similarly, lactoferrin supplementation without probiotics decreased all LOS (RR: 0.43; 95% CI: 0.29–0.62; I 2 = 0%; 8 studies; 1,209 subjects; LOE: moderate) significantly. Lactoferrin supplementation did not significantly reduce necrotizing enterocolitis (RR: 0.62; 95% CI: 0.29–1.33; I 2 = 43%; 6 studies; 3,079 subjects; LOE: low), all-cause mortality (RR: 0.74; 95% CI: 0.36–1.53; I 2 = 53%; 8 studies; 3,395 subjects; LOE: very low), bronchopulmonary dysplasia (RR: 1; 95% CI: 0.90–1.13; I 2 = 0%; 4 studies; 2,570 subjects; LOE: moderate), and threshold retinopathy of prematurity eligible for surgical treatment (RR: 0.61; 95% CI: 0.25–1.51; I 2 = 74%; 2 studies; 2,481 subjects; LOE: very low). Conclusion Low to moderate quality evidence suggests that lactoferrin supplementation reduces LOS in preterm infants. Further research is needed to improve the certainty in the evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".