P829 The Burden of Colorectal Cancer in Patients with Ulcerative Colitis – Incidence and Risk factors from a Population-based Inception Cohort from Veszprem county, Western Hungary, from 1977–2020
Bibliographic record
Abstract
Abstract Background Limited data are available on the incidence and risk of colorectal cancer (CRC) in ulcerative colitis (UC) from population-based studies in Eastern Europe. We aimed to identify the long-term incidence trends and predictors of CRC in a prospective population-based inception cohort from Veszprem, Western Hungary. Methods Patient inclusion for the inception cohort was between January 1,1977 and December 31,2018, and follow-up ended December 31,2020. The risk of CRC was estimated using standardized incidence ratios (SIRs). Age-and gender-specific CRC rates of the background population were derived from the National Cancer Registry. Results A total of 1,370 incident UC patients were included [male:51.2%, median age at diagnosis:37years). During a median follow-up of 17 years(IQR9-24), CRC was diagnosed in 41 UC patients(2.99%), equaling 1.76/1000 person-year(py).Median age at CRC diagnosis was 59(IQR:50.0-67.5) years. Overall SIR of CRC was 2.02(CI95%:10.1-12.1). SIRs were higher in extensive colitis (SIR:3.77, CI95%:2.41-5.91). The cumulative probability of CRC at 10-,20 and 30 years was 0.9% (95%CI0.6-1.2), 3.5%(95%CI2.8-4.2), and 6.5%(95%CI5.4-7.6), and there was no difference in the risk of CRC over different decades of UC diagnosis in a Kaplan Meier analysis(Log-rank=0.693). In multivariate analyses, co-existing PSC(HR4.19;95%CI1.72-10.20), colonic dysplasia(3.42;95%CI1.04-11.26), and extensive colitis (HR2.15;95%CI1.29-3.60) were identified as a significant predictor for CRC. Conclusion We report an increased CRC risk in UC patients with a standardized incidence ratio of approximately 2 folds, with a stable CRC risk over four decades. Disease extent, co-existing PSC and dysplasia were identified as predictors of CRC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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, 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".