Month of Birth & Childhood Asthma
Bibliographic record
Abstract
Background: Studies in the United States and Europe show that children born in fall-winter have higher risk of developing atopic status later in life. This study examines the relationship between month of birth and development of atopic status at 3 years of age across Canada. Method: The data were obtained from the Canadian Healthy Infant Longitudinal Development (CHILD) study. Data about month of birth, exposure to second hand smoking, mold, pet and cold were extracted from self reported questionnaire. Exposure to Nitrogen dioxide (NO2) was calculated by averaging the concentration of NO2 for the first six month of life for each participant. In total, 2367 children of approximately 3 years of old including 338 atopic individuals that had complete data on date of birth, atopic status and study location were included. The logistic regression run to do bivariate analysis and build the final model. Results: Results suggest that children born in June and December have higher risk of developing atopic status at three years old, though this result was not significant. Conclusion: Further research is needed to investigate seasonal pollen pattern and its association with atopic status. These results could be used to implement preventive measures for early management of childhood asthma.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".