Indications for Elective Caesarean Section in IBD: A Population-based Study
Bibliographic record
Abstract
Purpose: Women with Inflammatory Bowel Disease (IBD) are at a higher risk of caesarean section; they undergo c-sections at a higher rate than non-IBD patients. The purpose of this study is to identify all indications for elective caesarean section (ECS) in a population-based cohort of pregnant women with IBD. Methods: We included all women in the Calgary Health Zone, ≥18 years old with IBD, who were admitted to an acute care facility between January 1, 2006 and December 31, 2009. These patients were identified with the Data Integration, Management, and Reporting discharge abstract database that had a diagnosis of IBD (CD: ICD-9-CM 555.X, ICD-10-CA K50.X; UC: ICD-9-CM 556.X, ICD-10-CA K51.X), and coded for a pregnancy delivery (ICD-9-CM 630.X - 679.X, ICD-10-CA O00.X - O99.X). All patients underwent a comprehensive chart review (n=127) to validate diagnosis and pregnancy outcomes. ECS was pre-defined as a caesarean section that was planned prior to the onset of symptoms of labor. Indications for ECS were recorded, and an individual could have more than one. A chi-squared test compared indication for ECS between ulcerative colitis (UC) and Crohn's disease (CD). Results: There were 34 patients with ECS: 27 CD and 7 UC. See Table 1. We did not observe a statistically significant difference in the indications for ECS between CD and UC patients.Table 1Conclusion: To our knowledge, this is the first report of the specific indications for ECS utilizing a population-based cohort. ECS were most often done due to previous abdominal surgery, either IBD or pregnancy-related. Given that IBD patients are often on immunomodulators and biologics, a prospective study is underway to capture post-caesarean complications: wound infections, antibiotic use, and extended stay in hospital.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".