Association between the timing of asthma diagnosis and medication use during pregnancy
Bibliographic record
Abstract
STUDY OBJECTIVE: To assess whether asthma medication use during pregnancy differs in women newly diagnosed with asthma early in pregnancy (first 19 weeks of pregnancy) compared to those newly diagnosed up to 2 years pre-pregnancy. DESIGN: A retrospective population-based cohort study. DATA SOURCE: To conduct this study, we used the Quebec Asthma and Pregnancy Database (QAPD) constructed by linking two administrative health databases from the province of Quebec (Canada): the Régie de l'Assurance Maladie du Québec and Maintenance et Exploitation des Données pour l'Étude de la Clientèle Hospitalière databases. PATIENTS: A cohort comprising pregnant women newly diagnosed with asthma at any time in the 2 years prior to pregnancy or during the first 19 weeks of pregnancy was selected from the QAPD. MEASUREMENTS AND MAIN RESULTS: week of pregnancy until delivery. Poisson regression was used to compare the rates of asthma medication use in women diagnosed pre-pregnancy versus early in pregnancy. The cohort included 1731 women newly diagnosed with asthma pre-pregnancy and 359 women newly diagnosed with asthma early in pregnancy. Women diagnosed early in pregnancy were more likely to use ICS (adjusted rate ratio: 1.9, 95% confidence interval (CI): 1.6-2.3) and SABA (adjusted rate ratio: 2.0, 95% CI: 1.7-2.4) from the 20th week of pregnancy until delivery than those newly diagnosed pre-pregnancy. No significant differences were observed in the use of ICS/LABA [adjusted rate ratio: 0.9, 95% CI: 0.7-1.3] and OCS [adjusted rate ratio: 0.8, 95% CI: 0.6-1.2]. CONCLUSION: The higher use of ICS and SABA observed in women newly diagnosed with asthma early in pregnancy may suggest a more persistent asthma phenotype caused by pregnancy-triggered hormonal changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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 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".