ReviewPediatric research2022
Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research.
Review in Pediatric research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Preventing Sepsis in Preterm Infants with Bovine Lactoferrin: A Randomized Trial Exploring Immune and Antioxidant Effects.Nutrients · 2025Trial
- Serum Vitamin D Levels at Birth and Late-Onset Neonatal Sepsis in Preterm Neonates: A Retrospective Exploratory Cohort Study.Children (Basel, Switzerland) · 2026Article
- Systems biology approaches to unravelling neonatal sepsis and potential for precision diagnostics and therapeutics.Frontiers in pharmacology · 2026Review
- Assessing neonatal Sequential Organ Failure (nSOFA) scores in suspected late-onset neonatal sepsis among preterm infants: implications for morbidity and mortality.BMJ paediatrics open · 2024Article
- Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.Paediatric drugs · 2024Review
- Antimicrobial peptide LL37 and regulatory T cell associated with late-onset sepsis in very preterm infants.iScience · 2024Article
- The potential role of heparin-binding protein in neonatal sepsis: research progress.Frontiers in cellular and infection microbiology · 2024Review
- Paediatric and neonatal sepsis and inflammation.Pediatric research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Late-onset neonatal sepsis (LONS) remains an important threat to the health of preterm neonates in the neonatal intensive care unit. Strategies to optimize care for preterm neonates with LONS are likely to improve survival and long-term neurocognitive outcomes. However, many important questions on how to improve the prevention, early detection, and therapy for LONS in preterm neonates remain unanswered. This review identifies important knowledge gaps in the management of LONS and describe possible methods and technologies that can be used to resolve these knowledge gaps. The availability of computational medicine and hypothesis-free-omics approaches give way to building bedside feedback tools to guide clinicians in personalized management of LONS. Despite advances in technology, implementation in clinical practice is largely lacking although such tools would help clinicians to optimize many aspects of the management of LONS. We outline which steps are needed to get possible research findings implemented on the neonatal intensive care unit and provide a roadmap for future research initiatives. IMPACT: This review identifies knowledge gaps in prevention, early detection, antibiotic, and additional therapy of late-onset neonatal sepsis in preterm neonates and provides a roadmap for future research efforts. Research opportunities are addressed, which could provide the means to fill knowledge gaps and the steps that need to be made before possible clinical use. Methods to personalize medicine and technologies feasible for bedside clinical use are described.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.