Epidemiology, Treatment Strategy, Natural Disease Course and Surgical Outcomes of Patients with Ulcerative Colitis in Western Hungary – A Population-based Study Between 2007 and 2018: Data from the Veszprem County Cohort
Bibliographic record
Abstract
BACKGROUND AND AIMS: The number of population-based studies in ulcerative colitis [UC] from Eastern Europe is limited. Our aim here was to analyse the incidence, prevalence, disease phenotype, treatment strategy, disease course and colectomy rates in a prospective population-based inception cohort including UC patients diagnosed between 2007 and 2018. The present study is a continuation of the Veszprem IBD cohort since 1977. METHODS: In total, 467 UC patients were included [male/female: 236/231; median age at diagnosis: 36 years, IQR: 25-54 years]. Both in-hospital and outpatient records were collected and comprehensively reviewed. The mean length of follow-up was 8.34 ± 3.6 years. Demographic data were derived from the Hungarian Central Statistical Office. RESULTS: The mean incidence rate was 11.02/105 person-years in this 12-year period. Prevalence was 317.79/105 persons in 2015. Disease extent at diagnosis was proctitis [E1] in 22.3%, left-sided colitis [E2] in 43.9% and extensive colitis [E3] in 33.8%. The probability of disease extent progression was 11.6% [SE: 1.8] after 5 years. The distribution of maximal therapeutic steps was 5-ASA in 46.9%, corticosteroids in 16.3%, immunosuppressives in 19.3% and biologicals in 16.5%. The probability of receiving biological therapy after diagnosis was 9.9% [SE: 1.4] at 3 years. The overall colectomy rate was 4.1% in the population. The probability of colectomy was 1.5% [SE: 0.6] at 1 year, 3.6% [SE: 0.9] at 5 years and 4.4% [SE: 1.0] at 10 years. CONCLUSIONS: The incidence of UC was high in Hungary, similar to high-incidence areas in Western Europe. Treatment strategies are in line with the biological era. The probability of progressing to proximal disease, and the medium- and long-term colectomy rates were both lower compared with data from Western European centres.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".