ReviewFrontiers in allergy2024
The future of food allergy diagnosis.
Review in Frontiers in allergy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Emerging Biomarkers in Pediatric Food Allergy: From Mechanistic Endotyping to Precision Diagnosis and Therapeutic Monitoring.Biomedicines · 2026Review
- Artificial Intelligence in Paediatric Allergy: From Diagnostic Support to Precision Medicine.Cureus · 2026Review
- Emerging molecular and environmental biomarkers of shrimp allergy in African Americans in the US.Frontiers in allergy · 2026Review
- Specific IgE to tropomyosin increases the diagnostic accuracy of shrimp allergy.Frontiers in allergy · 2026Article
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- Basophil Activation Test in IgE-Mediated Wheat Allergy: Diagnostic and Clinical Applications-A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Predicting first-time anaphylaxis in the elderly using stacked machine learning and population registers.Frontiers in allergy · 2025Article
- Editorial: Prediction of severity of food allergy.Frontiers in allergy · 2025Article
- Scientific developments in understanding food allergy prevention, diagnosis, and treatment.Frontiers in immunology · 2025Review
- Prediction of food allergy reaction severity: biomarkers and host factors.Frontiers in allergy · 2025Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food allergy represents an increasing global health issue, significantly impacting society on a personal and on a systems-wide level. The gold standard for diagnosing food allergy, the oral food challenge, is time-consuming, expensive, and carries risks of allergic reactions, with unpredictable severity. There is, therefore, an urgent need for more accurate, scalable, predictive diagnostic techniques. In this review, we discuss possible future directions in the world of food allergy diagnosis. We start by describing the current clinical approach to food allergy diagnosis, highlighting novel diagnostic methods recommended for use in clinical practice, such as the basophil activation test and molecular allergology, and go on to discuss tests that require more research before they can be applied to routine clinical use, including the mast cell activation test and bead-based epitope assay. Finally, we consider exploratory approaches, such as IgE glycosylation, IgG4, T and B cell assays, microbiome analysis, and plasma cytokines. Artificial intelligence is assessed for potential integrated interpretation of panels of diagnostic tests. Overall, a framework is proposed suggesting how combining established and emerging technologies can effectively enhance the accuracy of food allergy diagnosis in the future.
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.