A228 RISANKIZUMAB - DRUG INDUCED LIVER INJURY; A CASE REPORT
Bibliographic record
Abstract
Abstract Background Risankizumab is a humanized monoclonal antibody to IL-23. It was initially used in the treatment of plaque psoriasis with recent approval to extend use to treat moderate to severe Crohn’s disease with evidence of endoscopic healing and clinical remission of disease. In the drugs safety manual, it has been reported to have transient liver enzyme elevation that is asymptomatic and not to a degree requiring drug discontinuation. No reported data on steatohepatitis. In this article we describe a 28 year old female, who had no evidence of liver disease in the past who underwent treatment with Risankizumab and within several months developed evidence of steatohepatitis with radiological evidence of significant hepatomegaly. Aims Case report details Drug induced liver injury to Risankizumab. Methods Case Report Results Drug induced liver injury with elevated liver enzyme levels and liver biopsy showing drug related liver changes likely in the setting of Risankizumab use. Conclusions Risankizumab is a newly approved drug in the setting of Crohn's disease, although described in the drug assessment report that it may cause transient hepatitis or eleveted liver enzymes in the setting of lower drug dose for Psoriasis use, the higher drug level used in the setting of Crohn's disease has not been formally evaluated and no previous known case reports of Drug induced Liver Injury to Risankizumab causing severe steatohepatitis Funding Agencies None
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".