ArticleTuberculosis and respiratory diseases2024
Big Data Research on Severe Asthma.
Article in Tuberculosis and respiratory diseases, 2024. 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
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
9 citing papers in PubMed, 13 citations in OpenAlex.
- Biologic Therapies in Severe Asthma and Eosinophilic Granulomatosis with Polyangiitis: Targeted Therapy and Personalized Care.Tuberculosis and respiratory diseases · 2026Article
- COVID-19 Pandemic and Decrease in New-Onset Asthma Incidence: National Data From Korea.Allergy · 2025Article
- Extended antibiotic exposure and exacerbations, mortality, and healthcare costs in asthma.Scientific reports · 2025Article
- Change in Exacerbation Rate of Asthma Patients before and after COVID-19 Infection.Tuberculosis and respiratory diseases · 2025Article
- Hospitalization, Underweight, Depression, and Cancer Increase the Risk of Suicide in Individuals With Bronchiectasis.Journal of Korean medical science · 2025Article
- Effects of Vaccination on Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Nationwide Population-Based Cohort Study.Tuberculosis and respiratory diseases · 2025Article
- Air Pollution Increases Healthcare Utilization Below Safe Thresholds in Individuals With Asthma.Allergy, asthma & immunology research · 2025Article
- International Severe Asthma Registry (ISAR): 2017-2024 Status and Progress Update.Tuberculosis and respiratory diseases · 2025Article
- Impact of physical activity on all-cause mortality in individuals with non-cystic fibrosis bronchiectasis.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The continuously increasing prevalence of severe asthma has imposed an increasing burden worldwide. Despite the emergence of novel therapeutic agents, management of severe asthma remains challenging. Insights garnered from big data may be helpful in the effort to determine the complex nature of severe asthma. In the field of asthma research, a vast amount of big data from various sources, including electronic health records, national claims data, and international cohorts, is now available. However, understanding of the strengths and limitations is required for proper utilization of specific datasets. Use of big data, along with advancements in artificial intelligence techniques, could potentially facilitate the practice of precision medicine in management of severe 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.