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Record W4212831269 · doi:10.1093/jcag/gwab049.163

A164 RISKS OF SKIN CANCERS PRE- AND POST-INFLAMMATORY BOWEL DISEASE DIAGNOSIS

2022· article· en· W4212831269 on OpenAlexaffabout
Mariam Narous, Zoann Nugent, Harminder Singh, Çharles N. Bernstein

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseSkin cancerUlcerative colitisBasal cell carcinomaEpidemiologyInternal medicineCancerDiseaseCohortPopulationDermatologySurveillance, Epidemiology, and End ResultsCancer registryMelanomaOncologyBasal cell

Abstract

fetched live from OpenAlex

Abstract Background Inflammatory bowel disease (IBD) has been shown to be associated with an increased risk of non-melanoma skin cancers (NMSC), specifically squamous cell carcinoma (SCC) and basal cell carcinoma (BCC), with the use of immunosuppressants such as thiopurines and tumor necrosis factor inhibitors (anti-TNFa). However, there is no literature exploring risk of skin cancers prior to formal Crohn’s disease (CD) or ulcerative colitis (UC) diagnoses. An increased risk pre IBD diagnosis could potentially suggest varied immunological impairments in patients with IBD. Aims To compare risks of NMSC and melanoma preceding and following a diagnosis of IBD and to evaluate the effect of thiopurines and anti-TNFa on risk of skin cancers in IBD. Methods This is a retrospective, historical cohort study using population-based data sources including the University of Manitoba IBD Epidemiology Database and the Manitoba Cancer Registry. Individuals with IBD diagnosed between 1987 and 2018 (N = 9344 / 636 NMSC) were identified and matched with randomly selected controls (N = 88916 / 3839 NMSC) based on age, sex, and postal area of residence on the date of IBD diagnosis (index date). Logistic and Cox regression analyses were performed to calculate adjusted risks skin cancers prior to and after IBD diagnosis. Results Subjects with IBD were more likely to have BCC pre-dating their IBD diagnosis (OR 1.50, 95% CI 1.24–1.81). Risks of SCC, other non-melanoma skin cancers, or melanoma prior to IBD diagnosis were not significantly increased. Post-IBD diagnosis, risk of BCC and SCC were significantly increased across all IBD groups (HR 1.60, 95% CI 1.45–1.76 and HR 1.65, 95% CI 1.37–1.99), except for SCC in UC. There was no significant association between melanoma and IBD post-IBD diagnosis. Thiopurines are associated with higher risks of BCC, SCC, and melanoma in IBD. Anti-TNFa use also raised risks of BCC and melanoma, but anti-TNFa alone did not increase risk of SCC in IBD. Nested case-control analysis confirmed a higher baseline risk of BCC in patient with IBD with censoring of both thiopurines and anti-TNFa. Similarly, censoring of both medications produced no effect on risk of SCC in IBD corroborating the absence of a baseline SCC risk in IBD. Conclusions The risk of BCC preceding a formal diagnosis of IBD is higher than in non-IBD controls, compared to a generally increased risk of NMSC post-IBD diagnosis. IBD is not associated with a significant risk of melanoma, although risk for melanoma is increased with thiopurine or anti-TNFa exposure. Our study suggests a possible inherent immune impairment in patients with IBD that leads to BCC. Thiopurine and anti-TNF therapy increase the risks for skin cancers evident in persons with IBD after their diagnoses. Funding Agencies None

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.006
GPT teacher head0.224
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

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