ReviewTranslational pediatrics2026
Early recognition and bundle-based management of pediatric sepsis in the emergency department: a narrative review.
Review in Translational pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Pediatric sepsis is a high-stakes, time-sensitive emergency, yet early recognition in the emergency department (ED) remains inconsistent because presenting features are heterogeneous, early signs are nonspecific, and implementation capacity varies across hospitals. At the same time, evidence for bundle-based care has expanded, but important clinical questions remain regarding which recognition approaches are most reliable in frontline ED settings, which bundle elements are best supported, and why pathway uptake remains uneven. This narrative review was undertaken to address these questions and to clarify current evidence gaps relevant to pediatric emergency practice. Methods: This structured narrative review searched PubMed on March 15, 2026 for studies published from January 1, 2010 through March 15, 2026 using terms related to pediatric sepsis, emergency care, screening, early recognition, sepsis bundles, biomarkers, quality improvement, and digital decision support. We focused on ED-relevant studies addressing recognition strategies, bundle implementation, nursing or workflow processes, and emerging technologies. Titles and abstracts were screened against predefined inclusion and exclusion criteria, and the final literature set was synthesized thematically into recognition, management, implementation, and future-direction domains. No language filter was planned at search design, but the review was limited to studies with accessible English full-text data for analysis. Key Content and Findings: Current evidence supports a layered ED recognition model that combines clinical assessment with structured screening, targeted biomarkers, and workflow-integrated digital support rather than reliance on any single tool. Evidence is strongest for bundle pathways improving process outcomes such as time to antibiotics, pathway activation, and compliance with first-hour care targets. By contrast, evidence for direct mortality benefit, stand-alone biomarker superiority, and routine use of artificial intelligence (AI)-enabled systems remains promising but inconsistent because studies are heterogeneous in definitions, populations, and implementation context. The literature also shows that success in general ED practice depends heavily on pediatric readiness, nursing-led escalation, local workflow design, and multidisciplinary coordination. Conclusions: Pediatric sepsis care in the ED should be organized around rapid recognition plus measurable, context-adapted bundle delivery. The clearest evidence gaps involve external validation of recognition tools in general EDs, more explicit evaluation of individual bundle components, harmonized operational definitions and bundle metrics, and implementation studies that test how digital support and nursing workflows can improve frontline performance.
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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.