Prevalence and Regional Factors in the Development and Course of Allergic Diseases in Children in Southern Kyrgyzstan
Bibliographic record
Abstract
Background: The prevalence of allergic diseases in children worldwide has increased rapidly over the past 30 years. This study aimed to identify regional factors influencing the development and course of allergic diseases for further prevention, control, and reduction of the risk and frequency of complications. Methods: To investigate the issue, 104 studies by different authors and countries, as well as topics related to allergy in children, air pollution, and regional factors of detection and prevalence of this disease in Kyrgyzstan, were selected. Of these, 52 studies were noted and analysed, which met the selection criteria and were of direct importance in this topic. Results: This study of allergic diseases in children found that more than 35% of children worldwide suffer from allergic diseases. Of these, allergic rhinitis occurs in 12% of children, atopic dermatitis is less common, but its incidence is 10-20%, and bronchial asthma, according to statistics, covers more than 14% of children. Conclusion: The results of the study helped to investigate the prevalence of allergic diseases relative to the region of residence, the impact of environmental pollution, geographical significance, and the effect of smoking on the development of allergies in children.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".