Analysis of Genotypic and Environmental Risk Factors for the Development of Allergic Diseases in Almaty Residents
Bibliographic record
Abstract
Allergic diseases represent a global health problems. According to the "Association of Allergists and ClinicalImmunologists" of Kazakhstan, the number of allergy sufferers in the country increases by 10-15% every year.Microarray genotyping has been widely used in allergy diagnosis due to its high throughput capability and specificity in identifying genetic markers associated with allergic diseases.This work is aimed to study the dynamics of the incidence of allergic diseases among residents of Almaty; assess the role of environmental and genotypic risk factors; identify marker genes related to asthma and allergic rhinitis and risk assessment in case and controls among patients.The findings revealed that allergic rhinitis was the most prevalent condition (58%), followed by bronchial asthma (9%).Self-reported allergy data were often inconsistent with medical diagnoses, underscoring the importance of clinical confirmation.A family history of allergies emerged as a major risk factor, significantly outweighing the impact of smoking.Genetic analysis identified specific SNPs in the IL13 and IL12B genes that were associated with increased allergy risk.For IL13, the T allele of rs1295686, A allele of rs848, and A allele of rs20541 were identified as significant.In IL12B, the T allele of rs2853694 and CC genotype of rs2569254 were linked to higher risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".