Prophylactic Probiotics for Preterm Infants: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
BACKGROUND: Necrotizing enterocolitis (NEC) is a major morbidity and cause of mortality in preterm neonates. Probiotics seem to have a beneficial role in preventing NEC, which is confirmed in meta-analyses of randomized controlled trials (RCTs). We therefore aimed to review and confirm the efficacy of probiotics in preterm neonates obtained in observational studies. OBJECTIVE: To assess the effects of prophylactic probiotics in preterm infants. METHODS: A meta-analysis was performed searching PubMed, EMBASE, CENTRAL (the Cochrane Library) and www.clinicaltrials.gov. Reference lists of reviews of RCTs were also searched. Included studies were observational studies that enrolled preterm infants <37 weeks of gestational age. Trials were included if they administered any probiotics and measured at least one clinical outcome (e.g. NEC, all-cause mortality, sepsis or long-term development scores). Two authors extracted characteristics and outcomes from included studies. The Newcastle-Ottawa Scale was used for quality assessment. A random-effects meta-analysis model was used, and heterogeneity was assessed by the I2 test. RESULTS: We included 12 studies with 10,800 premature neonates (5,144 receiving prophylactic probiotics and 5,656 controls). The meta-analysis showed a significantly decreased incidence of NEC (risk ratio, RR = 0.55, 95% confidence interval, 95% CI, 0.39-0.78; p = 0.0006) and mortality (RR = 0.72, 95% CI, 0.61-0.85; p < 0.0001). Sepsis did not differ significantly between the two groups (RR = 0.86, 95% CI, 0.74-1.00; p = 0.05). CONCLUSIONS: Probiotic supplementation reduces the risk of NEC and mortality in preterm infants. The effect sizes are similar to findings in meta-analyses of RCTs. However, the optimal strain, dose and timing need further investigation.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".