Asthma and Pregnancy: When You Have to Think of Two (Or More) Instead of One
Bibliographic record
Abstract
Asthma is the most common chronic disease in pregnancy affecting 3 to 12% of women.Poor asthma control is associated with adverse outcomes for both the mother and child. Unfortunately, nearly half of asthmatics discontinue or alter their asthma medication during pregnancy leading to diminished asthma control, and increased risks for mother and fetus from asthma exacerbation, manifesting most often in the middle-to-latter third of gestation. The triggers for asthma exacerbation are the same with or without pregnancy, mainly non-adherence to medications and viral infections. A significant association exists between severe asthma exacerbations in the first trimester and congenital malformations in the fetus as demonstrated in a 2015 publication in which the prevalence of any congenital malformation was 19.1%, 11.7% and 12.0% among women with severe, moderate and no such exacerbations during the first trimester, respectively. The adjusted OR for all malformations in this retrospective cohort study was 1.64 (95% CI 1.02 to 2.64) including cleft lip or palate, heart malformations and spina bifida when women with severe exacerbations were compared with those in the reference group, while no association was seen for moderate exacerbations. Asthma exacerbation can lead to maternal hypoxia which in turn affects fetal development and has been linked to congenital malformations. Accordingly, appropriate treatment in this patient population is essential.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".