P635 Incidence, prevalence, disease phenotype and maximal therapeutic step in Ulcerative Colitis patients in Western Hungary – a population-based study between 2007–2018, data from the Veszprem County cohort
Bibliographic record
Abstract
Abstract Background Recent trends indicate a change in the epidemiology of inflammatory bowel disease (IBD) with previously low incidence areas now reporting progressive rise. The number of epidemiological population-based studies are still limited from Eastern Europe. The present study is a continuation of the Veszprem IBD population-based cohort with a follow-up of the incidence and disease course of IBD since 1977. Our aim was to analyze the incidence and disease phenotype at diagnosis, and the maximal therapeutic step distributions during follow-up in a prospective population-based database from Veszprem Province, including incident ulcerative colitis (UC) patients diagnosed between January 1, 2007 and December 31, 2018. Methods Data of 467 incident UC patients were analyzed (male/female:236/231; median age at diagnosis:36 years(y)[IQR: 25–54]). Both in-hospital and outpatient records were collected and comprehensively reviewed at diagnosis and during clinical follow-up. The source of age- and gender-specific demographic data for statistical analysis was derived from the results of a national population census in 2011. Results Adjusted mean incidence rate was 11.02(CI95%:10.1–12.1)/105 person-years in this 12 year period[males:11.53(CI95%:10.2–13.1)/105; females:10.54(CI95%:9.3–12.0)/105]. Prevalence rate was 283.51(CI95%:265.7–300.7)/105 persons in 2011 and 317.79(CI95%:298.8–335.8) in 2015. For detailed annual incidence rates and age-specific mean incidence rates see Figure 1 and 2. Peak onset age in UC was 20–29 years of age. Diagnostic delay from symptom onset was ≥1year in 19.1%, and ≥3 years in 4.1% of the patients. Disease extension was proctitis(E1) in 22.3%, left sided colitis(E2) in 43.9%, and extensive colitis(E3) in 33.8% of the patients at diagnosis. Patients with extensive colitis were younger(32.9y,SD:16.6) at diagnosis compared to patients with proctitis(40.1y,SD:15.9;p<0.001) or left sided colitis(44.0y;SD:19.1;p<0.001). Extraintestinal manifestations were as follows: hepatic5.4%(PSC 2.1%), arthritis10.7%, ocular0.9%, skin3.6%. Active smokers at diagnosis were 12.6%. Distribution of maximal therapeutic steps during the course of follow-up (8.34y,SD: 3.5) were 5-ASA in 46.9%, systemic corticosteroids in 16.3%, immunosuppressive therapy in 19.3%; anti-TNF biologic therapy in 12.2% and other biologic therapy in 4.3%. Conclusion The incidence of UC in this 12 year period was high, comparable to high-incidence areas in Western European countries, and comparable to previous data from the Veszprem cohort, while prevalence rates continue to rise compared to our previous data.(Lakatos 2011, IBD). Disease extension at diagnosis and distribution of highest treatment step was comparable to European population-based data.(Burisch 2019, JCC).
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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".