Multidisciplinary Approach To The Problem Of Managing Children With Frequent Respiratory Infections Of The Upper Respiratory Tract: Key Results
Bibliographic record
Abstract
Background. Recurrent upper respiratory diseases in children are an urgent and common issue in pediatrics. Comorbidities often lead to prolonged, complicated, or chronic course of the inflammatory process in the upper airways. Objective. The aim of the study — scientific evidence and complex technology development for the management of children with recurrent upper respiratory diseases according to implemented multidisciplinary personalized approach, complex of modern methods for diagnosing and health monitoring to achieve long-term remission. Materials and methods. The study included 115 children aged from 2 years 6 months to 17 years 11 months with recurrent upper respiratory diseases. Examination included: nasal cavity, nasopharynx and larynx endoscopy, abdomen ultrasound with aqueous-siphon test, tympanometry, and laboratory tests (total and specific IgE levels, antistreptolysin-O, microbiological study of nasopharynx and oropharynx discharge, enzyme-linked immunosorbent assay of feces on Helicobacter pylori. Results. The study has revealed high prevalence of associated allergic pathology in children with recurrent upper respiratory diseases. Allergic rhinitis was the most common (55 (48%) children). There was also a high prevalence of gastroesophageal reflux disease (GERD) (37 (32%) children). Clinical signs of GERD were revealed in patients with chronic diseases significantly more often than in children with acute ENT pathology (27 (38%) and 10 (23%) children, respectively). It confirms the role of this comorbid condition in the development and course of chronic inflammatory process of the upper airways. Obtained data analysis has shown high prevalence of allergic and gastrointestinal pathology in children with recurrent upper respiratory diseases. Diagnostic and management algorithm for patients with recurrent upper respiratory diseases as well as practical guidelines were prepared based on the study data. This algorithm will allow to implement the personalized multidisciplinary approach for the management of prior and comorbid diseases via modern informative diagnostic methods. The complex approach to timely physical examination and pathogenetic treatment will ensure control of the patient’s condition and prevent the development of chronic ENT organ pathology 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".