Prepregnancy asthma and the subsequent risk of central nervous system defects in offspring
Bibliographic record
Abstract
BACKGROUND: The relationship between childhood asthma and central nervous system defects in offspring is poorly understood. We assessed if childhood asthma was associated with the risk of having an infant with neural tube or other nervous system defects compared with asthma during pregnancy. METHODS: We analyzed a longitudinal cohort of 128,060 women who were 5 years or less at study entry and later delivered an infant in Quebec, Canada (1989-2014). We identified women hospitalized for asthma before pregnancy, including childhood and adolescence, and determined if asthma was present during pregnancy based on obstetric records. Main outcomes were neural tube and non-neural tube defects in pregnancy. We used log-binomial regression models to determine risk ratios (RR) and 95% confidence intervals (CI) for the association between asthma and risk of nervous system defects, adjusting for patient characteristics. RESULTS: Asthma was associated with a greater risk of neural tube defects in offspring (RR 2.39, 95% CI 1.03-5.53) compared with no asthma, but not non-neural tube defects (RR 1.10, 95% CI 0.71-1.71). Women whose asthma resolved before pregnancy had a greater risk of neural tube defects (RR 3.43, 95% CI 1.35-8.69), while women with asthma during pregnancy were at greater risk of non-neural tube defects, especially microcephaly (RR 2.80, 95% CI 1.23-6.40). CONCLUSIONS: Asthma that resolved before pregnancy was associated with an increased risk of neural tube defects in offspring but not non-neural tube defects. Further investigation is needed to determine the pathophysiology connecting childhood asthma with nervous system defects in offspring.
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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 | 0.001 | 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".