Beta<sub>2</sub>‐agonists use during pregnancy and the risk of congenital malformations
Bibliographic record
Abstract
BACKGROUND: Treatment of asthma symptoms during pregnancy is crucial for maternal and fetal health. Short-acting beta2-agonists (SABA) are frequently used as rescue medications and long-acting beta2-agonists (LABA) are used as add-on controller therapy for asthma during pregnancy. OBJECTIVE: The objective of this study was to investigate the association between exposure to SABA and LABA in the first trimester of pregnancy and the risk of congenital malformations among women with asthma. METHODS: A cohort of pregnancies from women with asthma was formed through linkage of three administrative databases from Québec, Canada. The primary outcomes were major and any congenital malformations. The primary exposures were exposure to SABA and LABA during the first trimester, while secondary exposure was weekly SABA doses. The associations between congenital malformations (any, major, and specific) and SABA and LABA exposure were assessed with generalized estimating equations models. RESULTS: From a group of 13,117 pregnancies, we identified 1242 and 762 infants with any (9.5%) and major (5.8%) congenital malformations, respectively. The adjusted odds ratios (95% confidence interval [CI]) for any malformations associated with the use of SABA and LABA were 1.04 (95% CI, 0.92-1.17) and 1.37 (95% CI, 0.92-2.17), respectively. The corresponding figures were 0.93 (95% CI, 0.80-1.08) and 1.31 (95% CI, 0.74-2.31) for major malformations. Significant increased risks of major "cardiac" and major "other and unspecified" congenital malformations were observed with LABA use. CONCLUSION: Our study supports the evidence of SABA safety during pregnancy, but more research is required to assess whether the increased risk of malformations among LABA users is due to the medication, bias by asthma severity, or chance alone.
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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 | 0.004 |
| 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".