ELFIN, the United Kingdom preterm lactoferrin trial: interpretation and future questions
Bibliographic record
Abstract
Results from previous studies have suggested that supplemental bovine lactoferrin (BLF) given to preterm infants (<32 weeks gestation) reduces late-onset sepsis (LOS) and necrotising enterocolitis (NEC). The Enteral Lactoferrin in Neonates (ELFIN) study, performed in the UK, aimed to further address this issue with a well powered double-blind placebo controlled trial of >2200 preterm infants. The results from ELFIN did not demonstrate a reduction in LOS or NEC, or several other clinically important measures. Of the 1093 infants, 316 (29%) in the intervention group developed late-onset sepsis versus 334 (31%) of 1089 in the control group, with an adjusted risk ratio of 0.95 (95% CI = 0.86–1.04; p = 0.233). Reasons for the differences in ELFIN trial results and other studies may include population differences, the routine use of antifungal prophylaxis in the UK, timing of administration of the lactoferrin in relation to disease onset, or specific properties of the lactoferrin used in the different trials. The UK National Institutes for Health Research funded “Mechanisms Affecting the Guts of Preterm Infants in Enteral feeding trials” (MAGPIE) study is further exploring the use of lactoferrin, and the results should be available soon.
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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 | 0.162 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".