Research on Depression in Children with Severe Bronchial Asthma: The Impact of Alexithymia and Somatic Symptoms
Bibliographic record
Abstract
Objective: The prevalence of depression in children with severe bronchial asthma is a significant concern due to its potential effects on illness burden and quality of life. This crosssectional study aims to explore the relationship between depression and severe bronchial asthma in children, focusing on the impact of alexithymia and somatic symptoms. Methods: The study includes a total of 186 children aged 6-14 years diagnosed with severe bronchial asthma between 2008 and 2022 in our institute. Alexithymia was assessed using the Toronto Alexithymia Scale—20 items (TAS-20). Somatization symptoms were measured using the children's somatization inventory (CSI). The Hamilton depression scale (HAMD) was used to evaluate depression. Spearman correlation analysis was used to describe the correlation between alexithymia, somatization symptoms, and depression. Results: Children with bronchial asthma are found to have a significantly higher prevalence of depression, estimated to be around 16.67%. Approximately 98.92% of children exhibit varying degrees of somatic symptoms. Approximately 3.23% of children have alexithymia. The Spearman correlation analysis revealed that somatic symptoms and alexithymia were positive correlated with the depression. The correlation coefficients were 0.986 and 0.981 (P < .01), respectively. moreover, according to the results of multiple linear regression analysis, somatization symptoms and alexithymia significantly affects depression in children with severe bronchitis asthma (P < .01). Conclusion: These findings suggest that children with severe bronchial asthma experience a higher prevalence of depression, impacting their overall quality of life. In addition, the presence of somatic symptoms is prevalent among these children, further contributing to the burden on their quality of life. Moreover, somatization symptoms and alexithymia have been identified as a significant factor positive affecting depression in this population. Addressing these factors in clinical interventions may be beneficial for improving the overall well-being in this population.
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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 | 0.001 |
| 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) | 0.001 | 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".