Exploring geographical variances in component‐resolved diagnosis within the Asia‐Pacific region
Bibliographic record
Abstract
Component-resolved diagnostics (CRD) have revolutionized allergy diagnosis, offering enhanced accuracy and insights into allergen sensitization patterns. This review explores geographical variances in CRD for food and aeroallergens across the Asia-Pacific region. We examine the varying prevalence of allergic diseases and the utility of CRD in diagnosing common food allergies, including peanut, shellfish, fish, wheat, and fruits. Notable differences in serum-specific (sp)IgE sensitization patterns and the clinical relevance of particular allergen components are observed between populations in Asian countries and those in Europe and the United States. For food allergies, the literature reports significant differences in allergen components and their diagnostic utility across various countries. Peanut allergy diagnostics, particularly Ara h 2 spIgE, show varying sensitivity and specificity between Asian and Western populations. In shellfish allergy, emerging allergens beyond tropomyosin are gaining importance in the Asia-Pacific region. Fish and wheat allergies also demonstrate unique sensitization patterns, emphasizing the need for region-specific diagnostic approaches. Regarding aeroallergens, pollen sensitization profiles vary widely across the region, influenced by local flora and climate, to influence symptoms of pollen food allergy syndrome. House dust mite allergens remain a significant concern, with high sensitization rates to major components like Der p 1, Der p 2, and the emerging Der p 23. The cross-reactivity between house dust mite and shellfish allergens is particularly relevant in this region. These findings emphasize the need for region-specific CRD approaches, considering local allergen profiles and sensitization patterns. As the prevalence of allergic diseases continues to rise in the Asia-Pacific, further research into locally relevant allergens and their components is crucial for improving diagnosis, patient management, and targeted immunotherapy strategies in this diverse region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".