Asthma health services utilisation before, during and after pregnancy: a population-based cohort study
Bibliographic record
Abstract
During pregnancy, females with asthma may be at higher risk of exacerbation. The objective of this study was to determine whether females with asthma in Ontario, Canada have increased health services utilisation (HSU) during pregnancy.Rates of asthma-specific, asthma-related and non-pregnancy-related HSU were calculated in a population-based cohort of pregnant females with asthma. Poisson regression with repeated measures was used to determine adjusted rate ratios and 95% confidence intervals of HSU during and 1 year after pregnancy, compared to the year before pregnancy.The cohort consisted of 103 976 pregnant females with asthma. Compared to the year prior to pregnancy, hospitalisation rates per 100 person-months during pregnancy increased 30% for asthma (from 0.016 to 0.020), 24% for asthma-related conditions (from 0.012 to 0.015) and decreased 37% for non-pregnancy-related conditions (from 0.24 to 0.15). Emergency department visits for asthma and asthma-related conditions did not increase significantly during pregnancy. During pregnancy, physician office visits decreased 19% for asthma (from 2.20 to 1.79), 10% for asthma-related conditions (from 9.44 to 8.47) and increased 74% for non-pregnancy-related conditions (from 56.4 to 98.2).Hospitalisations for asthma and asthma-related conditions increased during pregnancy, demonstrating that the overall increase in non-pregnancy-related physician office visits may not meet the primary care needs of pregnant females with asthma.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".