SynthesisBMJ open2023
Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review.
Synthesis in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed, 8 citations in OpenAlex.
- Distinguishing common respiratory pathogens using machine learning of symptom profiles to prioritize diagnostic testing.PLOS digital health · 2026Article
- Research on age-stratified prevention and control strategies for pediatric RSV infection based on disease burden disparities.BMC infectious diseases · 2026Article
- RSV-infected children with mixed infections: clinical features and early predictive indicators of codetection withFrontiers in pediatrics · 2026Article
- Predicting regional and temporal incidence of RSV and influenza hospitalizations in a birth cohort of young Australian children.Scientific reports · 2025Article
- Predictive value of serological markers and immune indicators combined with early warning scoring system for prognosis in pediatric acute respiratory infections.Journal of medical biochemistry · 2025Article
- Epidemiological and clinical characteristics of bacterial co-detection in respiratory syncytial virus-positive children in Wenzhou, China, 2021 to 2023.BMC infectious diseases · 2025Observational
- Antibiotic use among young, hospitalized children in Jordan, 2010-2023.Microbiology spectrum · 2025Article
- External Validation of Brief Resolved Unexplained Events Prediction Rules for Serious Underlying Diagnosis.JAMA pediatrics · 2025Article
- Multiplex PCR and Antibiotic Use in Children with Community-Acquired Pneumonia.Children (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTo systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children.
designSystematic review. DATA SOURCES: PubMed and Embase were searched from 1 January 1975 to 3 February 2022. ELIGIBILITY CRITERIA: We included diagnostic models predicting viral ARIs in children (<18 years) who sought medical attention from a healthcare setting and were written in English. Prediction model studies specific to SARS-CoV-2, COVID-19 or multisystem inflammatory syndrome in children were excluded. DATA EXTRACTION AND SYNTHESIS: Study screening, data extraction and quality assessment were performed by two independent reviewers. Study characteristics, including population, methods and results, were extracted and evaluated for bias and applicability using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and PROBAST (Prediction model Risk Of Bias Assessment Tool).
resultsOf 7049 unique studies screened, 196 underwent full text review and 18 were included. The most common outcome was viral-specific influenza (n=7; 58%). Internal validation was performed in 8 studies (44%), 10 studies (56%) reported discrimination measures, 4 studies (22%) reported calibration measures and none performed external validation. According to PROBAST, a high risk of bias was identified in the analytic aspects in all studies. However, the existing studies had minimal bias concerns related to the study populations, inclusion and modelling of predictors, and outcome ascertainment.
conclusionsDiagnostic prediction can aid clinicians in aetiological diagnoses of viral ARIs. External validation should be performed on rigorously internally validated models with populations intended for model application. PROSPERO REGISTRATION NUMBER: CRD42022308917.
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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.