Liver diseases associated with anti-tumor necrosis factor-alpha (TNF-α) use for inflammatory bowel disease
Bibliographic record
Abstract
The conventional treatment of inflammatory bowel disease (IBD) has focused on nonspecifically targeting mucosal inflammation. In the last decade, with the advent of novel biological agents that directly inhibit proinflammatory cytokines, such as tumor necrosis factor alpha (TNF-α), rapid progress has been made in clinical management of complex and challenging patients with IBD. However, there remain many unanswered questions about the short and long-term side effects; this article focuses on hepatic complications. This review aims to provide a concise update to gastroenterologists on the well-known, as well as the potential rare consequences of anti-TNFα therapy on the liver and recommendations for clinical management. We performed a focused literature review for reports of the effect of anti-TNF therapy on preexisting liver disease as well as de novo hepatitis and drug-induced hepatotoxicity. Search terms used included anti-TNF therapy, biologics, liver disease, inflammatory bowel disease, hepatitis, hepatotoxicity, opportunistic infections,, and hepatitis virus reactivation. There are multiple potential effects of anti-TNF therapy on the liver during treatment of patients with IBD. Often treatment may be complicated by preexisting chronic liver disease. Clinicians should be aware of potential hepatic side effects and appropriate management options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".