The Epidemiology of Colectomy in Ulcerative Colitis: Results From a Population-Based Cohort
Bibliographic record
Abstract
OBJECTIVES: Previous studies have reported colectomy rates of over 50% in ulcerative colitis (UC), although changes in management may have influenced the rates of colectomy in the modern era. We sought to determine the incidence of colectomy in UC and identify risk factors associated with early colectomy (EC) and late colectomy (LC). METHODS: We used the University of Manitoba Inflammatory Bowel Disease Epidemiology Database, a population-based data set including UC patients with up to 25 years of post diagnosis follow-up. We tracked the occurrence of total colectomy in all patients with known UC, subdivided into EC (≤90 days from diagnosis date) and LC (>90 days from diagnosis). Survival curves were created and stratified by age, sex, era of diagnosis, and inpatient/hospital diagnosis. Cox proportional hazards modeling was used to determine which risk factors were predictive of either EC or LC. RESULTS: Among 3,752 patients with UC, 367 underwent colectomy. The 5-, 10- and 20-year actuarial risk of requiring colectomy was 7.5%, 10.4%, and 14.8%, respectively. Male sex (hazard ratio (HR): 2.63, [corrected] 95% confidence interval (CI): 1.58-4.36) and being initially diagnosed during a hospitalization (HR: 12.46, 95% CI: 7.40-21.0) were predictive of EC after adjustment for confounders. In-hospital diagnosis was predictive of LC, whereas being diagnosed more recently was protective against LC (HR: 0.96, 95% CI: 0.93-0.98). CONCLUSIONS: The cumulative incidence of colectomy in UC is lower than previously reported, and appears to be decreasing further among more recently diagnosed cohorts of patients. Male sex and hospitalization at the time of diagnosis are major risk factors for EC and LC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".