Risks of Melanoma and Nonmelanoma Skin Cancers Pre– and Post–Inflammatory Bowel Disease Diagnosis
Bibliographic record
Abstract
BACKGROUND: We compared risks of nonmelanoma skin cancers (NMSCs) and melanoma preceding and following a diagnosis of inflammatory bowel disease (IBD) and to evaluate the effect of thiopurines and anti-tumor necrosis factor α (anti-TNF-α) on skin cancer risk in IBD. METHODS: This was a retrospective, historical cohort study using the population-based University of Manitoba IBD Epidemiology Database (11 228 IBD cases and 104 725 matched controls) linked to the Manitoba Cancer Registry. Logistic and Cox regression analyses were performed to calculate skin cancer risks prior to and after IBD diagnosis. RESULTS: Persons with ulcerative colitis (UC) were more likely to have basal cell carcinoma (BCC) predating their UC diagnosis (odds ratio, 1.32; 95% confidence interval [CI], 1.08-1.60). Risks of squamous cell carcinoma (SCC), other NMSCs, or melanoma prior to IBD diagnosis were not significantly increased. Post-IBD diagnosis, risks of BCC (hazard ratio, 1.53; 95% CI, 1.37-1.70) and SCC (hazard ratio, 1.61; 95% CI, 1.29-2.01) were significantly increased across all IBD groups except for SCC in UC. There was no significant association between melanoma and IBD post-IBD diagnosis. The risks of BCC and melanoma were increased in thiopurine and anti-TNF users, and risk of SCC was increased in only thiopurine users. Nested cohort analysis of persons with IBD with censoring at both thiopurines and anti-TNF use confirmed a higher baseline risk of BCC and no effect on SCC, comparable to pre-IBD diagnosis findings. CONCLUSIONS: The risk of BCC preceding a diagnosis of UC is higher than in non-UC controls, compared with a generally increased risk of all NMSCs post-IBD diagnosis. Thiopurine and anti-TNF therapy increase the risks for skin cancers in persons with IBD after their diagnoses.
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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 | 0.001 |
| 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 | 0.000 | 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".