Evidence map›Paper›PMID 42536404›Full record

ArticleJMIR pediatrics and parenting2026

Feasibility of a German High-Frequency Neonatal and Pediatric Intensive Care Dataset (AIx-Neo-Guard Dataset): Secondary Data Cohort Study.

Lena Sophie Olivier, Camelia Lauterbach Oprea, André Stollenwerk, Thorsten Orlikowsky, Mark Schoberer

Abstract read
In one paragraph

Article in JMIR pediatrics and parenting, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lena Sophie OlivierDivision of Neonatology, Department of Pediatric and Adolescent Medicine, Uniklinik RWTH Aachen, Aachen, Germany.ORCID https://orcid.org/0000-0003-0169-4510
Camelia Lauterbach OpreaChair of Embedded Software (Computer Science 11), RWTH Aachen University, Aachen, Germany.ORCID https://orcid.org/0009-0006-0159-0200
André StollenwerkChair of Embedded Software (Computer Science 11), RWTH Aachen University, Aachen, Germany.ORCID https://orcid.org/0000-0002-9195-535X
Thorsten OrlikowskyDivision of Neonatology, Department of Pediatric and Adolescent Medicine, Uniklinik RWTH Aachen, Aachen, Germany.ORCID https://orcid.org/0000-0001-7123-0629
Mark SchobererDivision of Neonatology, Department of Pediatric and Adolescent Medicine, Uniklinik RWTH Aachen, Aachen, Germany.ORCID https://orcid.org/0000-0002-6827-1849

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAvailable information about existing neonatal and pediatric intensive care datasets is scarce.

objectiveThe objective was to evaluate the feasibility of a monocentric high-frequency dataset of neonatal and pediatric intensive care patients in a cohort study.

methodsThis study included patients treated in the neonatal and pediatric tertiary care intensive care unit at Uniklinik RWTH Aachen, Germany. The dataset comprised high-frequency data on bedside monitoring and ventilation, CO

resultsIn total, 400 admissions (227 neonatal and 173 pediatric) from 359 patients were included between September 2024 and January 2026. Of these, 28 neonates were extremely preterm, 34 were very preterm, 68 were moderate to late preterm, and 97 were term infants, with an overall median gestational age of 35 (IQR 31-38) weeks and a median birth weight of 1805 (IQR 1446-3251) g. The pediatric population comprised 34 infants aged younger than 1 year, 45 toddlers aged 1 to 5 years, 43 children aged 6 to 12 years, and 51 adolescents aged 13 to 18 years, with a median age of 10.6 (IQR 1.2-13.0) years and a median admission weight of 22 (IQR 11-50) kg. Overall, 77% (400/517) of all eligible patients were enrolled in the study. The dataset comprised 3600 patient days with more than 20,000 manual annotations (approximately 3 TB) and has been used for multiple use cases.

conclusionsThe high temporal resolution allows for detailed characterization of patient states. To our knowledge, there are no published descriptions of comparable European high-frequency neonatal or pediatric intensive care datasets. We share information about our dataset to encourage cross-institutional cooperation. The multicenter pooling of data or federated learning increases possible use cases by enabling new research questions or the development of more robust algorithms.

Indexed as

critical caredatabasedata warehousingmechanical ventilationneonatal intensive care unitsneonatologypediatric intensive care unitspediatrics

Identifiers

PMID42536404
PMCPMC13476729

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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.