The frequency of sleep-disordered breathing in preschool children with asthma and its effects on control of asthma
Bibliographic record
Abstract
CONCLUSION: The frequency and score of SDB were higher in patients with uncontrolled asthma. Frequency and score of SDB were significantly affected by the severity of asthma. SDB must be evaluated in preschool children with uncontrolled asthma. CONCLUSION: Sleep-disordered breathing (SDB) is more common in asthmatic patients than in non-asthmatic persons, and SDB affects negatively to control asthma. A limited number of studies are discovered on the effect of SDB in preschool asthmatic children. In this study, we aimed to investigate the prevalence of SDB and its effect on control and severity of asthma in preschool children. A pediatric sleep questionnaire was completed by parents of asthmatic children. Patients who received a score of 0.33 or higher were diagnosed with SDB. Control and severity of asthma was assessed by a pediatric allergy specialist based on the Global Initiative for Asthma (GINA) criteria. The study included 249 patients, with a mean±SD age of 4.37±1.04 (range: 2-5.9) years; 69% were boys; 56.6% children had uncontrolled asthma and 28.7% had SDB. The SDB score was significantly different between controlled and uncontrolled asthma (0.19 vs 0.28; P < 0.001). The frequency of uncontrolled asthma in patients with and without SDB was 74.3% and 49.4%, respectively (P < 0.010). Based on the severity of asthma, the frequency of SDB among patients with mild, moderate, and severe asthma was 23.4%, 35.2%, and 47.4%, respectively (P = 0.010).
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 | 0.001 |
| 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 | 0.000 | 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".