A Population-Based Matched Cohort Study of Digestive System Cancer Incidence and Mortality in Individuals With and Without Inflammatory Bowel Disease
Bibliographic record
Abstract
INTRODUCTION: To study digestive system cancer risks in individuals with inflammatory bowel diseases (IBDs) in the biologic era. METHODS: We used population-level administrative and cancer registry data from Ontario, Canada, (1994-2020) to compare people with IBD to matched controls (1:10 by sex and birth year) on trends in age-sex standardized cancer incidence and risk ratios of incident cancers and cancer-related deaths. RESULTS: Among 110,919 people with IBD and 1,109,190 controls, colorectal cancer incidence (per 100,000 person-years) declined similarly in people with ulcerative colitis (average annual percentage change [AAPC] -1.81; 95% confidence interval [CI] -2.48 to -1.156) and controls (AAPC -2.79; 95% CI -3.44 to -2.14), while small bowel cancer incidence rose faster in those with Crohn's disease (AAPC 9.68; 95% CI 2.51-17.3) than controls (AAPC 3.64; 95% CI 1.52-5.80). Extraintestinal digestive cancer incidence rose faster in people with IBD (AAPC 3.27; 95% CI 1.83-4.73) than controls (AAPC -1.87; 95% CI -2.33 to -1.42), particularly for liver (IBD AAPC 8.48; 95% CI 4.11-13.1) and bile duct (IBD AAPC 7.22; 95% CI 3.74-10.8) cancers. Beyond 2010, the incidences (and respective mortality rates) of colorectal (1.60; 95% CI 1.46-1.75), small bowel (4.10; 95% CI 3.37-4.99), bile duct (2.33; 95% CI 1.96-2.77), and pancreatic (1.19; 95% CI 1.00-1.40) cancers were higher in people with IBD. DISCUSSION: Cancer incidence is declining for colorectal cancer and rising for other digestive cancers in people with IBD. Incidence and mortality remain higher in people with IBD than controls for colorectal, small bowel, bile duct, and pancreatic cancers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".