Inflammatory bowel disease among patients with psoriasis treated with ixekizumab: A presentation of adjudicated data from an integrated database of 7 randomized controlled and uncontrolled trials
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease (IBD) occurs more frequently in patients with psoriasis. The 2 diseases have significant genetic overlap, but the pathogenesis underlying their co-occurrence is unknown. OBJECTIVE: We sought to report adjudicated IBD cases (Crohn's disease [CD] and ulcerative colitis [UC]) in patients exposed to ixekizumab, a high-affinity monoclonal antibody that selectively targets interleukin-17A. METHODS: Adverse events (AEs) integrated from 7 randomized controlled and uncontrolled trials were analyzed for the controlled induction period, controlled maintenance period, and all ixekizumab-treated patients. Suspected IBD cases were reviewed by blinded external experts using internationally recognized criteria (Registre Epidemiologique des Maladies de l'Appareil Digestif registry). RESULTS: In all, 4209 patients (6480 patient-exposure years) were exposed to ixekizumab. Suspected CD (N = 12) or UC (N = 17) AEs were reported; 19 were adjudicated as definite/probable IBD (CD, N = 7, incidence rate = 1.1/1000 patient-exposure years; UC, N = 12, incidence rate = 1.9/1000 patient-exposure years). Among these, 3 occurred during induction (CD, N = 1; UC, N = 2) and 7 during maintenance (CD, N = 4; UC, N = 3). Twelve of 16 patients with reported IBD history have not had an IBD treatment-emergent AE/serious AE to date. LIMITATIONS: Clinical review (adjudication) was not prespecified. AE data collected post-hoc may have been limited by length of time from occurrence. CONCLUSION: From an integrated database of 7 ixekizumab psoriasis trials, CD and UC cases were uncommon (<1%).
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".