Managing Pregnant Patients with Inflammatory Bowel Disease: A Difficult Compromise
Bibliographic record
Abstract
In the March issue of JCC, in the paper entitled ‘Inflammatory bowel disease patients are at higher risk of gestational diabetes’, Leung et al.1 analysed pregnancy outcomes of 116 inflammatory bowel disease (IBD) patients matched by age at conception with 381 pregnant women without IBD. In this Canadian cohort, after accounting for age and smoking status, IBD patients were independently at higher risk of gestational diabetes (odds ratio [OR] = 4.3; 95% CI 1.2–16.3; p = 0.03), preterm birth (OR = 19.7; 95% CI 2.2–173.9; p = 0.007) and C-section mode of delivery (OR = 2.7; 95% CI 1.6–4.6; p = 0.0002). Disease activity, overall use of IBD medication and specific use of thiopurines or biologicals were not associated with adverse pregnancy outcomes; however, the use of systemic corticosteroids among IBD patients slightly increased the risk of gestational diabetes (OR = 4.5; 95% CI 1.2–16.8; p = 0.03). In the subanalysis, the risk of gestational diabetes was significantly increased only among IBD patients using systemic corticosteroids, although a trend was present also in IBD patients without this treatment. Unfortunately, the retrospective design of the study precludes better identification of IBD patients at risk of steroid-induced gestational diabetes; more specifically, no dose- or timing-depending effects of corticosteroids could have been analysed in this cohort.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".