Role of Periostin in Uncontrolled Asthma in Children (DADO study)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Asthma is the most common chronic disease in children. Cases of severe asthma (SA) are underdiagnosed. Periostin is a biomarker for SA in adults, but its role in children is poorly understood. Objectives: The aims of the study were to estimate the percentage of cases of uncontrolled severe asthma (UcSA) in children with poorly controlled asthma and to evaluate the role of periostin as a biomarker. MATERIAL AND METHODS: We performed an observational study in children aged 5 to 14 years with poorly controlled asthma. Demographic and clinical data were collected in addition to the results of the lung function test, the fraction of exhaled nitric oxide, the skin prick test, total IgE, specific IgE, blood eosinophil count, serum periostin, treatment, asthma control, and quality of life. Variables were compared between the group with UcSA and the other children. RESULTS: Fifty children with poorly controlled asthma (72% male) were included. Nineteen children (38%) had UcSA. Most children had limitations in their activities of daily living and had visited the emergency department. In addition, 38% were hospitalized. Quality of life was poor. Only 42% of the children received appropriate treatment. The UcSA group was more likely to have a total IgE >500 kUA/mL (52.6% vs 19%, P=.02) and less likely to have serum periostin >1000 ng/mL (31.2% vs 63%, P=.04). CONCLUSIONS: In our setting, 38% of children with poorly controlled asthma have UcSA, which is associated with higher levels of total serum IgE and lower levels of serum periostin.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".