Interventions for reducing late-onset sepsis in neonates: an umbrella review
Bibliographic record
Abstract
OBJECTIVES: Neonatal sepsis is one of the leading causes of neonatal deaths in neonatal intensive care units. Hence, it is essential to review the evidence from systematic reviews on interventions for reducing late-onset sepsis (LOS) in neonates. METHODS: PubMed and the Cochrane Central were searched from inception through August 2020 without any language restriction. Cochrane reviews of randomized clinical trials (RCTs) assessing any intervention in the neonatal period and including one or more RCTs reporting LOS. Two authors independently performed screening, data extraction, assessed the quality of evidence using Cochrane Grading of Recommendations Assessment, Development and Evaluation, and assessed the quality of reviews using a measurement tool to assess of multiple systematic reviews 2 tool. RESULTS: A total of 101 high-quality Cochrane reviews involving 612 RCTs and 193,713 neonates, evaluating 141 interventions were included. High-quality evidence showed a reduction in any or culture-proven LOS using antibiotic lock therapy for neonates with central venous catheters (CVC). Moderate-quality evidence showed a decrease in any LOS with antibiotic prophylaxis or vancomycin prophylaxis for neonates with CVC, chlorhexidine for skin or cord care, and kangaroo care for low birth weight babies. Similarly, moderate-quality evidence showed reduced culture-proven LOS with intravenous immunoglobulin prophylaxis for preterm infants and probiotic supplementation for very low birth weight (VLBW) infants. Lastly, moderate-quality evidence showed a reduction in fungal LOS with the use of systemic antifungal prophylaxis in VLBW infants. CONCLUSIONS: The overview summarizes the evidence from the Cochrane reviews assessing interventions for reducing LOS in neonates, and can be utilized by clinicians, researchers, policymakers, and consumers for decision-making and translating evidence into clinical practice.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".