Perinatal and early life factors and asthma control among preschoolers: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Preventing poor childhood asthma control is crucial for short-term and long-term respiratory health. This study evaluated associations between perinatal and early-life factors and early childhood asthma control. METHODS: This retrospective study used administrative health data from mothers and children born 2010-2012 with a diagnosis of asthma before age 5 years, in Alberta, Canada. The outcome was asthma control within 2 years after diagnosis. Associations between perinatal and early-life factors and risk of partly and uncontrolled asthma were evaluated by multinomial logistic regression. RESULTS: Of 7206 preschoolers with asthma, 52% had controlled, 37% partly controlled and 12% uncontrolled asthma 2 years after diagnosis. Compared with controlled asthma, prenatal antibiotics (adjusted risk ratio (aRR): 1.19; 95% CI 1.06 to 1.33) and smoking (aRR: 1.18; 95% CI 1.02 to 1.37), C-section delivery (aRR: 1.11; 95% CI 1.00 to 1.25), summer birth (aRR: 1.16; 95% CI 1.00 to 1.34) and early-life hospitalisation for respiratory illness (aRR: 2.24; 95% CI 1.81 to 2.76) increased the risk of partly controlled asthma. Gestational diabetes (aRR: 1.41; 95% CI 1.06 to 1.87), C-section delivery (aRR: 1.18; 95% CI 1.00 to 1.39), antibiotics (aRR: 1.32; 95% CI 1.08 to 1.61) and hospitalisation for early-life respiratory illness (aRR: 1.65; 95% CI 1.19 to 2.27) were associated with uncontrolled asthma. CONCLUSION: Maternal perinatal and early-life factors including antibiotics in pregnancy and childhood, gestational diabetes, prenatal smoking, C-section and summertime birth, and hospitalisations for respiratory illness are associated with partly or uncontrolled childhood asthma. These results underline the significance of perinatal health and the lasting effects of early-life experiences on lung development and disease programming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".