Allergic Risk Among the Children in Southern China: The Association of Influencing Factors with the Allergen Distribution
Bibliographic record
Abstract
Jieyan Ma,1 Genfeng Wu,2 Heming Huang,1 Gaochi Li3 1Clinical Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Affiliated Shenzhen Women and Children’s Hospital (Longgang) of Shantou University Medical College), Shenzhen, Guangdong, People’s Republic of China; 2Department of Paediatrics, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Affiliated Shenzhen Women and Children’s Hospital (Longgang) of Shantou University Medical College), Shenzhen, Guangdong, People’s Republic of China; 3Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Affiliated Shenzhen Women and Children’s Hospital (Longgang) of Shantou University Medical College), Shenzhen, Guangdong, People’s Republic of ChinaCorrespondence: Gaochi Li, Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Affiliated Shenzhen Women and Children’s Hospital (Longgang) of Shantou University Medical College), Shenzhen, Guangdong, People’s Republic of China, Email leecoach198009@126.comBackground: Due to the inability of children with allergies to exhibit appropriate clinical symptoms, pediatricians often face the challenge of accurately diagnosing allergic diseases in children. Identifying the distribution of allergens is essential for the effective diagnosis and treatment of allergic diseases.Methods: We investigated the distribution of 28 allergens among 12,292 suspected allergic children in Shenzhen, whose serum-specific IgE was subjected to relevance analysis with influencing factors.Results: The results showed the overall allergen distribution was 66.36%. Mite, cow’s milk, and egg white were the most prevalent allergens. Indoor allergens are significantly higher than outdoor allergens. There was extensive cross-reactivity among homologous species of allergens such as crustacean allergens, plant-derived allergens, etc. A 14KDa profilin as a common ingredient is suspected to be the main cause of the cross-reactivity among these plant-derived allergens. The frequencies of mite, cow’s milk and egg white showed different trends with growing age, indicating that the frequencies of allergens are age-related. Various mechanisms of immune systems of children mature at different ages. We found that the proportion of mite sensitivity was highest in children with allergic rhinitis and conjunctivitis, while the proportion of cow’s milk and egg white sensitivity was higher in children with allergic dermatitis such as eczema and urticaria.Conclusion: Age and cross-reactivity play important roles in diagnosing allergies in children. Children at different ages exhibit varying sensitivities to different types of allergens, and identifying cross-reactions helps to comprehensively understand children’s allergic status. Pediatricians can develop corresponding prevention and management measures based on allergen results and clinical symptoms. Keywords: allergen distribution, correlation, cross-reactivity, age, allergic symptoms
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".