SynthesisBMC medical informatics and decision making2015
A systematic review of predictive models for asthma development in children.
Synthesis in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled 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.
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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review.BMC medical informatics and decision making · 2021Pooled it
- Models for estimating and projecting global, regional and national prevalence and disease burden of asthma: a systematic review.Journal of global health · 2020Pooled it
- Trial
- Machine learning-based early prediction of asthma in preschoolers: The COCOA birth cohort study.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Article
- Asthma and severe acute respiratory infections: a stratified analysis of mortality patterns in Brazil.BMC public health · 2025Article
- Temporal trends and future projections of incidence rate and mortality for asthma in China: an analysis of the Global Burden of Disease Study 2021.Frontiers in medicine · 2025Article
- Article
- Biomarkers in allergen immunotherapy: Focus on eosinophilic inflammation.Asia Pacific allergy · 2024Review
- Chinese expert consensus on the diagnosis, treatment, and management of asthma in women across life.Journal of thoracic disease · 2024Review
- The CHANGE (Climate Health ANalysis Grading Evaluation) tool for weight of evidence reviews on climate change and health research.Environmental health : a global access science source · 2024Article
- Article
- Early Prediction of Asthma.Journal of clinical medicine · 2023Review
- Network analyses of upper and lower airway transcriptomes identify shared mechanisms among children with recurrent wheezing and school-age asthma.Frontiers in immunology · 2023Article
- Implementing predictive tools in surgery: A narrative review in the context of orthopaedic surgery.ANZ journal of surgery · 2022Review
- The Pediatric Asthma Risk Score: More does not mean better.Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology · 2022Article
- Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma.Children (Basel, Switzerland) · 2021Article
- Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.Journal of medical Internet research · 2021Article
- Personalized prediction of early childhood asthma persistence: A machine learning approach.PloS one · 2021Article
- Is the predictive model for asthma development useful as a tool for diagnosing pediatric asthma?Clinical and experimental pediatrics · 2020Article
- Association between peripheral blood mononuclear cellThe Journal of international medical research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAsthma is the most common pediatric chronic disease affecting 9.6 % of American children. Delay in asthma diagnosis is prevalent, resulting in suboptimal asthma management. To help avoid delay in asthma diagnosis and advance asthma prevention research, researchers have proposed various models to predict asthma development in children. This paper reviews these models.
methodsA systematic review was conducted through searching in PubMed, EMBASE, CINAHL, Scopus, the Cochrane Library, the ACM Digital Library, IEEE Xplore, and OpenGrey up to June 3, 2015. The literature on predictive models for asthma development in children was retrieved, with search results limited to human subjects and children (birth to 18 years). Two independent reviewers screened the literature, performed data extraction, and assessed article quality.
resultsThe literature search returned 13,101 references in total. After manual review, 32 of these references were determined to be relevant and are discussed in the paper. We identify several limitations of existing predictive models for asthma development in children, and provide preliminary thoughts on how to address these limitations.
conclusionsExisting predictive models for asthma development in children have inadequate accuracy. Efforts to improve these models' performance are needed, but are limited by a lack of a gold standard for asthma development in children.
Indexed as
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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.