ReviewFrontiers in pediatrics2018
Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice.
Review in Frontiers in pediatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- A meta-analysis of genome-wide association studies of childhood wheezing phenotypes identifieseLife · 2023Pooled it
- Pediatric Mild Asthma: Bridging Current Gaps to Improve Future Care.Pediatric pulmonology · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Clustering lung function and symptom profiles for asthma risk stratification.Scientific reports · 2025Article
- Patterns of Respiratory Symptoms and Asthma Diagnosis in School-Age Children: Three Birth Cohorts.Allergy · 2025Article
- Unsupervised discovery of clinical disease signatures using probabilistic independence.Journal of biomedical informatics · 2025Article
- Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.BMJ open · 2024Article
- Identification of eczema clusters and their association with filaggrin and atopic comorbidities: analysis of five birth cohorts.The British journal of dermatology · 2023Article
- Molecular Mapping of Allergen Exposome among Different Atopic Phenotypes.International journal of molecular sciences · 2023Article
- Characteristics of Allergic, Eosinophilic, and Overlapping Asthma Phenotypes Among Pediatric Patients with Current Asthma: A Cross-Sectional Study from Saudi Arabia.Journal of asthma and allergy · 2023Article
- Modeling Wheezing Spells Identifies Phenotypes with Different Outcomes and Genetic Associates.American journal of respiratory and critical care medicine · 2022Article
- Genetics of Asthma and Allergic Diseases.Handbook of experimental pharmacology · 2022Article
- Allergen Management in Children with Type 2-High Asthma.Journal of asthma and allergy · 2022Review
- Childhood CCL18, CXCL10 and CXCL11 levels differentially relate to and predict allergy development.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2021Article
- Four childhood atopic dermatitis subtypes identified from trajectory and severity of disease and internally validated in a large UK birth cohort.The British journal of dermatology · 2021Article
- The Hygiene Hypothesis and New Perspectives-Current Challenges Meeting an Old Postulate.Frontiers in immunology · 2021Review
- Quantitative and semiquantitative estimates of mold exposure in infancy and childhood respiratory health.Environmental epidemiology (Philadelphia, Pa.) · 2020Article
- Distinguishing Wheezing Phenotypes from Infancy to Adolescence. A Pooled Analysis of Five Birth Cohorts.Annals of the American Thoracic Society · 2019Article
- The Syndrome We Agreed to Call Bronchiolitis.The Journal of infectious diseases · 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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in big data analytics have created an opportunity for a step change in unraveling mechanisms underlying the development of complex diseases such as asthma, providing valuable insights that drive better diagnostic decision-making in clinical practice, and opening up paths to individualized treatment plans. However, translating findings from data-driven analyses into meaningful insights and actionable solutions requires approaches and tools which move beyond mining and patterning longitudinal data. The purpose of this review is to summarize recent advances in phenotyping of asthma, to discuss key hurdles currently hampering the translation of phenotypic variation into mechanistic insights and clinical setting, and to suggest potential solutions that may address these limitations and accelerate moving discoveries into practice. In order to advance the field of phenotypic discovery, greater focus should be placed on investigating the extent of within-phenotype variation. We advocate a more cautious modeling approach by "supervising" the findings to delineate more precisely the characteristics of the individual trajectories assigned to each phenotype. Furthermore, it is important to employ different methods within a study to compare the stability of derived phenotypes, and to assess the immutability of individual assignments to phenotypes. If we are to make a step change toward precision (stratified or personalized) medicine and capitalize on the available big data assets, we have to develop genuine cross-disciplinary collaborations, wherein data scientists who turn data into information using algorithms and machine learning, team up with medical professionals who provide deep insights on specific subjects from a clinical perspective.
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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.