Clinical studies of lactoferrin in children<sup>1</sup>This article is part of a Special Issue entitled Lactoferrin and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
Much has been learned in recent years about the mechanisms by which breastfeeding improves child health and survival. However, there has been little progress in using these insights to improve pediatric care. The aim of this study was to review all clinical studies of lactoferrin (LF) in children in an effort to determine which interventions may improve pediatric care or require further research. We conducted a systematic and critical review of published literature and found 19 clinical studies that have used human or bovine LF for different outcomes: iron metabolisms and anemia (6 studies), fecal flora (5 studies), enteric infections (3 studies), common pediatric illnesses (1 study), immunomodulation (3 studies), and neonatal sepsis (1 study). Although the efficacies have varied in each trial, the main finding of all published studies is the safety of the intervention. Protection against enteric infections and neonatal sepsis are the most likely biologically relevant activities of LF in children. Future studies on neonatal sepsis should answer critically important questions. If the data from these sepsis studies are proven to be correct, it will profoundly affect the treatment of low birth weight neonates and will aid in the reduction of child mortality worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".