ArticleThe Journal of allergy and clinical immunology2017
Disaggregating asthma: Big investigation versus big data.
Article in The Journal of allergy and clinical immunology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of clinical prediction models to support the diagnosis of asthma in primary care.NPJ primary care respiratory medicine · 2019Pooled it
- Differential associations of allergic disease genetic variants with developmental profiles of eczema, wheeze and rhinitis.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2019Trial
- Features of asthma which provide meaningful insights for understanding the disease heterogeneity.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2018Trial
- Review
- Next Decade Research in Asthma: Broad Omics-Based Exploration Versus Targeted Airway Epithelium Studies.Journal of clinical medicine · 2025Review
- How Families Manage the Home Environment for Young People With Asthma and Allergic Sensitisation: A Qualitative Study.Pediatric pulmonology · 2025Article
- Data-driven research on eczema: Systematic characterization of the field and recommendations for the future.Clinical and translational allergy · 2022Article
- Genetics of Asthma and Allergic Diseases.Handbook of experimental pharmacology · 2022Article
- A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma.Children (Basel, Switzerland) · 2021Article
- Influential Usage of Big Data and Artificial Intelligence in Healthcare.Computational and mathematical methods in medicine · 2021Review
- Article
- Machine and deep learning meet genome-scale metabolic modeling.PLoS computational biology · 2019Review
- Data Science for Child Health.The Journal of pediatrics · 2019Review
- Causes of variability in latent phenotypes of childhood wheeze.The Journal of allergy and clinical immunology · 2019Article
- Blood Biomarkers of Sensitization and Asthma.Frontiers in pediatrics · 2019Review
- Epidemiology of Asthma in Children and Adults.Frontiers in pediatrics · 2019Review
- Evolution of IgE responses to multiple allergen components throughout childhood.The Journal of allergy and clinical immunology · 2018Article
- Article
- Clinical prediction models to support the diagnosis of asthma in primary care: a systematic review protocol.NPJ primary care respiratory medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand causal mechanisms and translating this knowledge into personalized prevention and management strategies. In recent years, "big data" has been sold as a panacea for generating hypotheses and driving new frontiers of health care; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of health care data and computational tools for data analysis is that the process of data mining can become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data, and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data- and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological, and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness bigger health care data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma.
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.