A123 NON-MELANOMA SKIN CANCER IN IBD PATIENTS TAKING 6-TGN ANTIMETABOLITES A POPULATION STUDY
Bibliographic record
Abstract
Non-melanoma skin cancer (NMSC) rates have been shown to increase in Inflammatory Bowel Disease (IBD) patients taking 6-thioguanine (6-TGN) antimetabolites (Azathioprine and 6-Mercatopurine). There is limited population-based data assessing the risk of NMSC in IBD patients on 6-TGN and anti-tumor necrosis factor (anti-TNF) therapy. To determine the risk of NMSC amongst IBD patients on 6-TGN antimetabolites and anti-TNF therapies in a longitudinal, population-based Canadian cohort This was a retrospective cohort study using population-based administrative data from Saskatchewan (1970 to 2011). IBD and NMSC cases were identified through application of validated administrative definitions to ICD billing codes. Using population-based drug dispensal data IBD cases were classified as “exposed” and “unexposed” to each of 6-TGN antimetabolites and anti-TNF therapies. Univariate and multivariate conditional logistic regression and cox-proportional hazard analyses were performed. Hazard ratios were adjusted for age, sex, location, and medication. A total of 8713 prevalent IBD cases were identified for inclusion, 51.6% CD and 48.4% UC. There were 349 cases of NMSC, 68 patients were exposed to 6-TGN and 281 non-exposed. The unadjusted hazard ratio (HR) was 0.74 in IBD patients exposed to 6-TGN compared to non exposed, p 0.0276 (95% CI 0.57–0.97). Stratified by age (<50 or 50 and older) the adjusted HR was 1.18 in IBD patients <50 years old, p 0.40 (0.8–1.74) and 1.38 ≥50 years old, p 0.1708 (0.87–2.20). The adjusted HR in CD patients < 50 years old was 1.08, p 0.7531 (0.67–1.73) and 1.78 ≥50 years old, p 0.030 (1.06–3.00). The adjusted HR in UC patients < 50 years old was 1.32, p 0.4232 (0.67–2.57) and 0.47 ≥50 years old, p 0.2624 (0.12 – 1.77) Overall, lower rates of NMSC in IBD patients taking 6-TGN compared to those not exposed to 6-TGN were observed. However when stratified for age CD patients greater than age 50 exposed to 6-TGN had statistically higher rates of NMSC compared to those not exposed, suggesting an age-related effect. Analyses to better understand the association between 6-TGN on NMSC risk which explore cumulative exposure to 6-TGN therapy and the impact of anti-TNF on NMSC will be performed. This study contributes additional data that further elucidates the relationship between 6-TGN exposure and development of NMSC Funding Agencies:
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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 | 0.001 |
| 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) | 0.002 | 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".