<scp>IL23</scp> Receptor Polymorphism Is a Predictor of Anti‐Tumour Necrosis Factor‐α‐Induced Paradoxical Psoriasis in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Paradoxical psoriasis (PP) is an adverse drug reaction associated with anti-tumour necrosis factor (TNF)-α therapy leading to disfiguring skin lesions that may impact an individual's quality of life and affect their inflammatory bowel disease (IBD) care. To date, there are no tools to identify individuals at risk for PP. IL-23 receptor (IL23R) gene polymorphisms have been linked to psoriasis and may be implicated in PP. AIMS: To evaluate the association between the IL23R1142G>A single nucleotide variant and the occurrence of PP in an anti-TNFα-treated IBD population and assess other clinical variables associated with PP development. METHODS: In a retrospective cohort study conducted in anti-TNFα-exposed adult patients with IBD, participants were screened for the IL23R1142G>A variant genotype and the occurrence of PP was assessed. Participants were additionally assessed for clinical variables associated with PP development and for the impact of PP on IBD treatment. RESULTS: Among 499 patients, the incidence of PP was 5.1% (29/570) with 570 unique anti-TNFα exposures. Most patients had severe PP (69.0%) and required treatment cessation (69.0%), which resulted in complete resolution of PP. An IL23R1142G>A variant genotype was highly associated with PP development (46.2% vs. 5.1%, OR = 17.8, 95% CI 7.8-40.0; p < 0.0001). No other clinical variables were associated with the occurrence of PP. CONCLUSIONS: Variation in the IL23R gene may identify those at risk of anti-TNFα-induced PP, beyond clinical variables. Further validation of this finding may promote its utility as a clinically actionable tool for the safe delivery of IBD medical therapy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".