Impact of asthma diagnosis during pregnancy on perinatal outcomes
Bibliographic record
Abstract
Background: While asthma during pregnancy is associated with adverse perinatal outcomes, it is not clear if this is due to a more severe phenotype that develops during pregnancy, which could put the fetus at a higher risk than pre-existing asthma. Aim: To assess if the risks of prematurity, low birth weight (LBW), small-for-gestational age (SGA), and major malformations are higher in women with asthma diagnosed during compared to before pregnancy. Methods: We conducted a retrospective cohort study of pregnant women aged 15 years from Quebec health administrative databases. New-onset asthma was defined as the first recorded diagnosis in 10 years. Timing of diagnosis was further classified as occurring in the 2 years prior to pregnancy-onset and every month thereafter until delivery. We used inverse probability weighted log-binomial models at each time point to estimate the absolute risks of preterm birth (delivery<37th week of gestation), LBW (≤2500g), SGA (BW<10th percentile), and major malformations among women with asthma diagnosed during compared to before pregnancy. Results: In a cohort of 122848 deliveries, the absolute risks for LBW and prematurity in women with new-onset asthma were elevated in the last few months of pregnancy compared to those previously diagnosed. There was no evident association for other perinatal outcomes. Conclusions: Asthma diagnosed in the last trimester is associated with an increased risk of preterm birth and LBW.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".