Perceptions and Attitudes Towards Medication Adherence during Pregnancy in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Women with inflammatory bowel disease [IBD] report concerns about medication safety during pregnancy. Adherence to IBD medications may be lower in pregnant patients as a result. The aim of this study was to assess medication adherence during pregnancy in women with inflammatory bowel disease. METHODS: Female patients of childbearing age completed a self-administered, structured survey. We collected demographic data, medication history, and self-reported adherence to IBD medications during pregnancy. We also assessed knowledge and perceptions of IBD medication safety in pregnancy. A time trade-off [TTO] analysis was done to assess health utilities for continuing or discontinuing IBD medications during pregnancy. RESULTS: A total of 204 women completed the survey [mean age was 32.8 years]. Current or previous pregnancy was reported by 101 patients [median parity 2, median gravity 1]. While pregnant or attempting to conceive, 47 [46.5%] participants reported stopping a prescribed IBD medication. Of those, 20 participants reported stopping medications without the advice of a physician. TTO analysis was completed by 31 patients. When presented with the option of continuing a potentially teratogenic medication, switching to less effective medication that is non-teratogenic, or stopping medication all together, participants consistently preferred to not remain on the most effective IBD therapy. CONCLUSIONS: Women with IBD report preference to not remain on IBD medications during pregnancy. This is driven by concerns about safety and uncertainty about teratogenic effects. Women with IBD may benefit from increased education about medication safety in pregnancy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".