ReviewPediatric research2026
Enabling evidence generation for extremely preterm neonates: the use of neonatal real-world data.
Review in Pediatric research, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extremely low gestational age neonates (ELGANs), born before 28 weeks of gestation, face the greatest burden of neonatal mortality and morbidity, yet remain among the most underserved populations in clinical research. Most medications administered in the neonatal intensive care unit lack adequate safety, efficacy, and dosing data, and no major therapeutic advances have improved preterm survival since the introduction of surfactant in the early 1990s. Real-world data (RWD) presents a promising opportunity to bridge these evidence gaps. ELGANs are ideal candidates for RWD research given the extraordinary density and granularity of data generated during prolonged neonatal intensive care unit stays, ethical and practical barriers to traditional clinical trials, and wide variations in clinical practice. In this review, we examine how ELGANs may benefit from RWD, describe the current landscape of common data models and their neonatal limitations, propose a minimum neonatal-specific variable set, and explore the roles of key stakeholders in building RWD infrastructure. We further highlight the regulatory applications of RWD including external controls, target trial emulation, pharmacovigilance, and natural history studies. Realizing the potential of RWD for ELGANs requires coordinated investment from clinicians, informaticians, regulators, and families to improve outcomes for the most vulnerable patients in medicine. IMPACT: ELGANs are an ideal population for RWD research given the extraordinary density of longitudinal NICU data, ethical and practical barriers to traditional randomized trials, and wide variations in evidence-based clinical practice. Current common data models were not designed with neonatal complexity in mind, lacking native support for maternal-infant linkage, continuous physiologic data, weight-based medication dosing, and long-term neurodevelopmental follow-up. A coordinated, multi-stakeholder framework involving neonatologists, bedside caregivers, informaticians, regulators, and families is essential to building the RWD infrastructure needed to improve outcomes for ELGANs.
Identifiers
42829373What OpenQuestion holds
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.