Features of Statistical Accounting of Allergic Diseases in Children — Evidence from Moscow
Bibliographic record
Abstract
Background. The emergence of new functional capabilities of statistical accounting made it possible to conduct a comparative analysis of the morbidity of allergic pathologies according to the registers of allergists and pediatricians from the Unified Medical Information and Analytical System (UMIAS) of Moscow with data from the Form of Federal Statistical Observation No. 12 (FSO No. 12). The aim of the study is to investigate the potential of using UMIAS for assessing/monitoring the morbidity of allergic diseases, including bronchial asthma in children, using the example of several outpatient clinics (OPCs) in Moscow. Methods. A study of combined design has been carried out. The data of children of several OPCs in Moscow were analyzed — data from UMIAS (observation registers of pediatricians and allergist-immunologists) and from the reporting forms of the FSO No. 12. Results. For a comparative analysis of statistical data from UMIAS and FSO No. 12, we studied the information of 60,851 children under 18 years of age. It was revealed that out of 60,851 children: allergic rhinitis according to FSO No. 12 and UMIAS was established in 1001 and 1059 patients; atopic dermatitis — in 142 and 345; urticaria — in 363 and 33; angioedema — in 4 and 16, respectively; food allergy — in 233 children according (to FSO No. 12) and in none of the children (according to UMIAS). Out of 60,851 children, 619 children were diagnosed with bronchial asthma according to the annual report (FSO No. 12) and 537 according to the pediatrician’s observation registers (UMIAS). At the same time, it was found that the diagnosis of bronchial asthma is not available as a separate nosology in the registry of an allergist-immunologist, and information about children with bronchial asthma is available to this specialist only when analyzing the uploaded information about children with other allergic diseases. Conclusion. A adequate sample ensured a high representativeness of the results obtained. The differences in the incidence rates of allergic diseases revealed by a comparative analysis of data from various sources — UMIAS and FSO No. 12 — indicate the need to improve both the system of statistical registration of incidence and the development of modern algorithms for early diagnosis and dynamic monitoring of children with allergies.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".