Is there a need for sub-specialist pregnancy clinics for women with inflammatory bowel disease (IBD)? The imapct of attendance at and geographic variation in access to the dedicated IBD pregnancy clinics in Alberta, Canada
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is commonly diagnosed during the childbearing years.1,2 Women with IBD have increased risks of adverse pregnancy and infant outcomes, and these risks are increased with active disease at conception, as this doubles the risk of disease relapse during pregnancy.3–6 Suboptimal knowledge and misperceptions on the impact of IBD on pregnancy may result in poor decision making, resulting in worsening of outcomes.7–9 Preconception counselling from dedicated IBD pregnancy clinics have been shown to increase IBD-specific pregnancy knowledge and improve IBD management during pregnancy.10,11Using five population-based administrative health care databases linked by unique maternal and neonatal identification numbers in Alberta, we found that approximately 30% of pregnant women with IBD attended a dedicated IBD pregnancy clinic. Women were more likely to attend if they resided in close vicinity to the first established clinic, were nulliparous, had a disease flare prior to pregnancy, and were on any maintenance IBD medications. Propensity score weights were used to adjust for baseline covariates and evaluate the impact of attendance on IBD-specific, pregnancy, and infant outcomes. Despite increased odds of active disease and IBD-related emergency department (ED) visits during pregnancy in women who attended the clinic compared to women who did not attend, there was no difference in postpartum disease activity or IBD-related ED visit on clinic attendance and infants born to mothers who attended had better health status at birth. Increased risk of scheduled caesarean delivery in women who attended the clinic may reflect the complex phenotype of women requiring sub-specialty care.Using geospatial analyses, we identified geographic disparities in maternal characteristics, adverse pregnancy outcomes, attendance at an IBD pregnancy clinic, the proportion of reproductive-aged women with IBD living in a given area, and local geographic access to gastroenterologists or obstetricians/gynecologists. Location-allocation analysis identified three potential locations for satellite IBD pregnancy clinics in the North and South Alberta Health Services (AHS) Zones to decrease travelling time for reproductive-aged women with IBD. Further work, however, is needed to identify the most optimal health care delivery model (e.g., telemedicine) with limited health care resources in these geographic regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".