Probiotics and Preterm Infants
Bibliographic record
Abstract
More than 10,000 preterm infants have participated in randomised controlled trials on probiotics worldwide, suggesting that probiotics in general could reduce rates of necrotising enterocolitis (NEC), sepsis, and mortality. Answers to relevant clinical questions as to which strain to use, at what dosage, and how long to supplement are, however, not available. On the other hand, an increasing number of commercial products containing probiotics are available from sometimes suboptimal quality. Also, a large number of units around the world are routinely offering probiotic supplementation as the standard of care despite lacking solid evidence. Our recent network meta-analysis identified probiotic strains with greatest efficacy regarding relevant clinical outcomes for preterm neonates. Efficacy in reducing mortality and morbidity was found for only a minority of the studied strains or combinations. In the present position paper, we aim to provide advice, which specific strains might potentially be used and which strains should not be used. In addition, we aim to address safety issues of probiotic supplementation to preterm infants, who have reduced immunological capacities and occasional indwelling catheters. For example, quality reassurance of the probiotic product is essential, probiotic strains should be devoid of transferable antibiotic resistance genes, and local microbiologists should be able to routinely detect probiotic sepsis. Provided all safety issues are met, there is currently a conditional recommendation (with low certainty of evidence) to provide either Lactobacillus rhamnosus GG ATCC53103 or the combination of Bifidobacterium infantis Bb-02, Bifidobacterium lactis Bb-12, and Streptococcus thermophilus TH-4 in order to reduce NEC rates.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".