Impact of maternal use of asthma-controller therapy on perinatal outcomes
Bibliographic record
Abstract
BACKGROUND: Asthma during pregnancy usually requires treatment with controller medications about which more safety information is needed. The objectives are to assess the impact of the use of long-acting β2-agonists (LABAs) and the dose of inhaled corticosteroids (ICSs) during pregnancy on the prevalence of low birth weight (LBW), preterm birth (PB) and small for gestational age (SGA). METHODS: A cohort of women with asthma giving birth from 1998 to 2008 was constructed from Québec (Canada) administrative databases. LBW was defined as weight <2500 g, PB as delivery before 37 weeks' gestation and SGA as a birth weight below the 10th percentile. The impact of the use of LABAs and the dose of ICSs during pregnancy on the outcomes was determined with generalised-estimating-equation models. RESULTS: The cohort included 7376 pregnancies: 8.8% exposed to LABAs and 56.9% exposed to ICSs. All LABA users also received ICSs. The prevalence of LBW, PB and SGA was 7.7%, 9.5% and 13.5%, respectively. LABA use was not found to be associated with increased prevalence of LBW (OR 0.81; 95% CI 0.58 to 1.12), PB (OR 0.84; 95% CI 0.61 to 1.15) or SGA (OR 0.92; 95% CI 0.70 to 1.20). Mean ICSs doses >125 μg/day (fluticasone-equivalent) were associated with a non-significant trend of increased LBW, PB and SGA. CONCLUSIONS: Despite the possibility of residual confounding due to uncontrolled or more severe asthma or smoking status, the use of LABA and low to moderate doses of ICSs were not associated with increased prevalence of perinatal outcomes. Additional research on higher ICSs doses is required to better evaluate their safety during pregnancy.
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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 | 0.007 |
| 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 | 0.001 | 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".