Efficacy of lactoferrin supplementation in pediatric infections: a systematic review and meta-analysis
Bibliographic record
Abstract
Pediatric infections account for approximately one-third of all deaths in children under 5 years globally. Lactoferrin (LF) supplementation has the potential to reduce infection-related morbidity due to its antimicrobial, anti-inflammatory, and immunoregulatory properties. We conducted a systematic review and meta-analysis of oral LF supplementation randomized controlled trials in population under 18 years old. The primary outcomes were infection-associated outcomes: late onset sepsis (LOS), diarrhea, and upper respiratory infections (URIs). We also analyzed mortality among LOS studies. Of 1594 citations identified, 25 studies met eligibility criteria, including 10 studies of LOS, 14 of diarrhea, and 8 of URI. LF supplementation was associated with fewer patients with culture-proven or probable neonatal LOS compared to placebo (odds ratio (OR): 0.60; 95% confidence interval (CI): 0.42-0.86), with fewer patients with diarrhea compared to placebo in children (OR: 0.56; 95% CI: 0.41-0.75), and no significant fewer patients with URI (OR: 0.61; 95% CI: 0.27-1.40). Before LF can be used as a public health intervention, it is necessary to refine some aspects of the design of future trials. Ideally these trials should be conducted in countries with the highest burden of infections, where the potential benefit is expected to have the largest impact.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".