Natural Disease Course of Ulcerative Colitis During the First Five Years of Follow-up in a European Population-based Inception Cohort—An Epi-IBD Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Few population-based cohort studies have assessed the disease course of ulcerative colitis [UC] in the era of biological therapy and widespread use of immunomodulators. The aim of this study was to assess the 5-year outcome and disease course of patients with UC in the Epi-IBD cohort. METHODS: In a prospective, population-based inception cohort of unselected patients with UC, patients were followed up from the time of their diagnosis, which included the collection of their clinical data, demographics, disease activity, medical therapy, and rates of surgery, cancers, and deaths. Associations between outcomes and multiple covariates were analysed by Cox regression analysis. RESULTS: A total of 717 patients were included in the study. During follow-up, 43 [6%] patients underwent a colectomy and 163 [23%] patients were hospitalised. Of patients with limited colitis [distal to the left flexure], 90 [21%] progressed to extensive colitis. In addition, 92 [27%] patients with extensive colitis experienced a regression in disease extent, which was associated with a reduced risk of hospitalisation (hazard ratio [HR]: 0.5 95% CI: 0.3-0.8]. Overall, patients were treated similarly in both geographical regions; 80 [11%] patients needed biological therapy and 210 [29%] patients received immunomodulators. Treatment with immunomodulators was found to reduce the risk of hospitalisation [HR: 0.5 95% CI: 0.3-0.8]. CONCLUSIONS: Although patients in this population-based cohort were treated more aggressively with immunomodulators and biological therapy than in cohorts from the previous two decades, their disease outcomes, including colectomy rates, were no different. However, treatment with immunomodulators was found to reduce the risk of hospitalisation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".