Effect of bovine lactoferrin on prevention of late-onset sepsis in infants <1500 g: a pooled analysis of individual patient data from two randomized controlled trials
Bibliographic record
Abstract
We previously conducted two randomized controlled trials with bovine lactoferrin (bLF) for the prevention of late-onset sepsis (LOS) in infants with a birth weight <2500 g (Study 1) and <2000 g (Study 2). The aim of this study was to determine the preventative effects of bLF on culture-proven or probable LOS in infants with a birth weight <1500 g from both studies, and to determine the effect of bLF in relation to intake of human milk. Both trial designs had similar inclusion and exclusion criteria, the same dose of bLF [200 mg·(kg body mass)–1·day–1], and used the same control (maltodextrin). We fitted multivariate Cox regression models to estimate the effect of bLF on the risk of development of the composite outcome, adjusting for covariates. We included 335 neonates with a mean birth weight of 1162 ± 244 g; 27.5% were <1000 g. There were 33 first episodes of LOS in the bLF treatment group and 48 in the control group (19.5% vs. 28.9%). bLF had a protective effect on the risk of development of LOS [hazard ratio (HR) = 0.64; %95 CI = 0.41–0.99; p = 0.048]; particularly among infants weighing <1000 g [HR = 0.46; %95 CI = 0.22–0.96; p = 0.039] and infants with a low intake of human milk [HR = 0.40; %95 CI = 0.19–0.84; p = 0.015]. Therefore, bLF supplementation protects infants <1500 g from LOS, particularly those infants not receiving human milk.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".