Factors affecting lactoferrin concentration in human milk: how much do we know?
Bibliographic record
Abstract
Lactoferrin (LF) is a breast milk glycoprotein with antimicrobial and anti-inflammatory effects. Its beneficial properties in infants, especially in those born preterm, are currently being studied in clinical trials. However, the maternal and nursing infant factors that may affect LF concentration in breast milk are still not clear. We conducted a systematic review to investigate the factors that may affect the concentration of LF in breast milk. We used a 2-step approach to identify the eligible studies according to inclusion/exclusion criteria, and to determine which studies would be considered. We included 70 qualified articles from 29 countries with publication dates ranging from 1976 to 2015. We described the correlation between LF concentration in breast milk and lactation stage; 10 maternal factors, such as race, parity, among others; and 2 infant factors: infections and prematurity. Colostrum has the highest LF levels, but they decrease with days postpartum. No other factor has been consistently associated with LF concentration. A major limitation of the majority of the published studies is the small sample size and the different methods used to measure LF concentration. Therefore, there is a need for large, multicenter studies with standardized study design, sample collection, and LF measurement methods to identify clinically significant factors associated with LF expression in breast milk, which will help promote exclusive breastfeeding in preterm infants.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".