Evaluation of Aeroallergen Sensitivities in Children with Asthma and Allergic Rhinitis
Bibliographic record
Abstract
Background: Asthma and allergic rhinitis are prevalent respiratory conditions influenced by genetic, environmental, and immunological factors. Understanding the distribution of these conditions and their associated allergens is essential for effective management and prevention strategies. This study aimed to investigate the prevalence of asthma, allergic rhinitis, and their co- occurrence in a specific population, with a focus on allergen sensitivity patterns. Methods: A retrospective analysis was conducted on patients diagnosed with asthma, allergic rhinitis, or both. Allergen sensitivity was assessed using skin prick tests for common environmental allergens, including weeds, mites, cockroaches, and mixed allergens. Data analysis included prevalence rates, allergen distribution, and a heatmap visualization of allergen-patient associations. Results: Males demonstrated a higher prevalence of asthma (68.8%), allergic rhinitis (64.4%), and combined conditions (67.0%) compared to females. Weeds were the most common allergen among asthma patients (40.0%), while mixed allergens were predominant among allergic rhinitis (45.7%) and coexisting conditions (50.0%). Heatmap analysis revealed strong associations between mites, cockroaches, and respiratory conditions, reinforcing their role as significant allergens. The high prevalence of mixed allergens in patients with both conditions suggests the need for comprehensive allergy management approaches. Conclusion: This study highlights a male predominance in asthma and allergic rhinitis cases and underscores the role of environmental allergens in disease manifestation. The findings support targeted allergen avoidance and personalized immunotherapy strategies for optimal respiratory disease management
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".