Evidence map›Paper›PMID 42871721›Full record

ReviewCureus2026

When Fever Hides Fire: A Systematic Review of Clinical and Digital Approaches to Early Recognition of Pediatric Sepsis.

Mohammed Elmostafa Khalid Abdulrahman, Ola Yassir Osman Abdalmagid, Abdulbagie Musaad Algylani Almardy, Osman Osama Osman Mohamed Elhassan, Ibraheem Abu Haltem, Asim Ahmed, Nahla Bakri Ageeb Babekr

Abstract readReview
In one paragraph

Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Mohammed Elmostafa Khalid AbdulrahmanDepartment of Pediatrics, King's College Hospital London - Dubai, Dubai, ARE.
Ola Yassir Osman AbdalmagidDepartment of Pediatric Medicine, Dongola Specialized Hospital, Dongola, SDN.
Abdulbagie Musaad Algylani AlmardyDepartment of Pediatrics, Maternity and Children Hospital, Buraidah, SAU.
Osman Osama Osman Mohamed ElhassanFaculty of Medicine and Surgery, Omdurman Islamic University, Omdurman, SDN.
Ibraheem Abu HaltemDepartment of General Medicine, Al-Aman Medical Center, Amman, JOR.
Asim AhmedFaculty of Medicine and Surgery, University of Gezira, Wad Madani, SDN.
Nahla Bakri Ageeb BabekrDepartment of Pediatrics, Sudan Medical Specialization Board, Khartoum, SDN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early recognition of pediatric sepsis remains challenging because clinical presentation, sepsis definitions, and recognition approaches vary across healthcare settings. This systematic review evaluated clinical and digital strategies for the early recognition of pediatric sepsis, including structured screening tools, electronic alerts, prediction models, machine-learning approaches, clinical decision support, and recognition pathways. Diagnostic criteria were considered separately as reference frameworks for classifying established sepsis and interpreting the performance of recognition tools. PubMed and Scopus were systematically searched, eligible studies were independently screened, data were extracted, and methodological quality and risk of bias were assessed using design-appropriate tools. Because of substantial clinical and methodological heterogeneity, the evidence was synthesized narratively. A total of 13 studies were included. Several clinical and digital approaches generally performed well in ruling out sepsis among low-risk children; however, their ability to identify true cases and limit unnecessary alerts varied according to alert thresholds, clinical settings, and the sepsis definition or reference standard used. No single approach was consistently superior for early recognition. Automated screening appeared most useful when integrated with structured clinical assessment rather than used independently. Differences in pediatric sepsis definitions substantially influenced reported performance, and implementation studies more consistently demonstrated improvements in recognition and care processes than in patient outcomes. The available evidence was limited by heterogeneous methods, imperfect reference standards, insufficient external validation, and risk of bias. Clinical and digital recognition systems may support earlier identification of children with suspected infection who require further assessment; however, current evidence does not establish a universally superior approach. Further prospective multicenter studies are needed to determine which strategies provide meaningful clinical benefit across diverse healthcare settings.

Indexed as

clinical decision supportearly diagnosismachine learningpediatric sepsissepsis screening

Identifiers

PMID42871721
PMCPMC13660302

What OpenQuestion holds

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Registered trials

None linked

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.