ArticleViruses2021
Symptom-Based Predictive Model of COVID-19 Disease in Children.
Article in Viruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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
7 citing papers in PubMed, 13 citations in OpenAlex.
- Integrated Deep Learning Surveillance of Unknown Pathogens with Pandemic Potential Using Pneumonia of Unknown Etiology.Pathogens (Basel, Switzerland) · 2026Article
- Causal networks guiding large language models: application to COVID-19.Health care management science · 2025Article
- Implementing Symptom-Based Predictive Models for Early Diagnosis of Pediatric Respiratory Viral Infections.Viruses · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
- Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Graph data science and machine learning for the detection of COVID-19 infection from symptoms.PeerJ. Computer science · 2023Article
- HIV HGM biobank as a research platform for paediatric infectious diseases and COVID-19 pandemic.AIDS research and therapy · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTesting for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms.
methodsEpidemiological and clinical data were obtained from the REDCap
resultsThe use of ML demonstrated an AUROC of 0.65 to predict a COVID-19 diagnosis in children. The absence of high-grade fever was the major predictor of COVID-19 in younger children, whereas loss of taste or smell was the most determinant symptom in older children.
conclusionsAlthough the accuracy of the models was lower than expected, they can be used to provide a diagnosis when epidemiological data on the risk of exposure to COVID-19 is unknown.
Indexed as
Identifiers
What 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.