Prevalence of Bronchial Asthma in Children in Southern Kyrgyzstan
Bibliographic record
Abstract
Background: The aim of this study was to investigate the prevalence of bronchial asthma and other allergic diseases such as allergic rhinitis and atopic dermatitis among 6075 school-aged children in Osh, Jalal-Abad and Batken regions of Kyrgyzstan. Methods: 6075 children were questioned using the ISAAC questionnaire. Bronchial asthma symptoms are frequent – 21.1% of children had difficulty wheezing, and 13.4% had night cough. This indicates a high prevalence of bronchial asthma among children in the study population. The obtained morbidity rates significantly exceed the official statistics for the region. This indicates insufficient diagnosis of allergic diseases in children and substantiates the need to develop a set of measures aimed at optimizing the detection of cases of allergic pathology and increasing the effectiveness of therapeutic and preventive measures. Results: The results of the study demonstrate a high need for the development and implementation of a regional program for the diagnosis, treatment, and prevention of allergic diseases in children of Osh, Jalal-Abad, and Batken regions. Conclusion: Implementing such a program will contribute to better control of bronchial asthma and other allergic pathologies and improve the quality of life of the child population in the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".