Impact of asthma diagnosis during pregnancy on perinatal outcomes
Bibliographic record
Abstract
<b>Background:</b> 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. <b>Aim:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".