Risk of Malignant Cancers in Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVES: To explore the trends and the predictors of incident malignant cancer among patients with inflammatory bowel disease [IBD]. METHODS: We identified a cohort of all patients with incident IBD in Quebec, Canada, from 1998 to 2015, using provincial administrative health-care databases [RAMQ and Med-Echo]. Annual incidence rates [IRs] of cancer were calculated using Poisson regression and were compared with those of the Quebec population using standardized incidence ratios [SIRs ]. Temporal trends in these rates were evaluated by fitting generalized linear models. Conditional logistic regression was used to estimate odds ratios [ORs] for predictors associated with cancer development. RESULTS: The cohort included 35 985 patients with IBD, of which 2275 developed cancers over a mean follow-up of 8 years (IR 785.6 per 100 000 persons per year; 95% confidence interval [CI] 754.0-818.5). The rate of colorectal cancer decreased significantly from 1998 to 2015 [p < 0.05 for linear trend], but the incidence remained higher than expected, compared with the Quebec population [SIR 1.39; 95% CI 1.19-1.60]. Rates of extraintestinal cancers increased non-significantly over time [p = 0.11 for linear trend]. In the IBD cohort, chronic kidney disease [OR 1.29; 95% CI 1.17-1.43], respiratory diseases [OR 1.07; 95% CI 1.02-1.12], and diabetes mellitus [OR 1.06; 95% CI 1.01-1.11] were associated with an increase in the incidence of cancer. CONCLUSIONS: The decreasing rates of colorectal cancer suggest improved management and care in IBD. Further studies are needed to explore the impact of comorbid conditions on the risk of cancer in IBD.
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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 | 0.002 |
| 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) | 0.002 | 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".