Increased Risk of Asthma in Children with ADHD: Role of Prematurity and Maternal Stress during Pregnancy.
Bibliographic record
Abstract
OBJECTIVE: ADHD and asthma are prevalent conditions in childhood, with complex pathophysiology involving genetic-environmental interplay. The study objective is to examine the prevalence of asthma in our ADHD population and explore factors that may increase the risk of developing asthma in children with ADHD. METHODS: We retrospectively analyzed the presence of maternal stress during pregnancy and history of asthma in 201 children diagnosed with ADHD. RESULTS: Chi-square analysis indicated significant higher presence of asthma in our ADHD sample compared to Quebec children, χ(2)(1, N = 201) = 15.37, P<0.001. Only prematurity and stress during pregnancy significantly predicted asthma in a logistic regression model, χ(2)(2)=23.70, P<0.001, with odds ratios of 10.6 (95% CI: 2.8-39.5) and 3.2 (95% CI: 1.4-7.3), respectively. CONCLUSION: Children with ADHD have a higher prevalence of asthma than the general Quebec pediatric population. Children with ADHD born prematurely and/or those whose mothers experienced stress during pregnancy have a significantly increased risk of developing asthma. The study highlights the importance of potentially offering social and psychological support to mothers who experienced stress during pregnancy and/or are at risk of delivering prematurely.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".