Review article: chronic viral infection in the anti‐tumour necrosis factor therapy era in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Anti-tumour necrosis factor (TNF) therapy is now well established in the treatment of inflammatory bowel disease and the risk of opportunistic infection is recognized. However, specific considerations regarding screening, detection, prevention and treatment of chronic viral infections in the context of anti-TNF therapy in inflammatory bowel disease are not widely adopted in practice. AIM: To provide a detailed and comprehensive review of the relevance of chronic viral infections in the context of anti-TNF therapy in inflammatory bowel disease. METHODS: Literature search was conducted using Medline, Pubmed and Embase using the terms viral infection, hepatitis, herpes, CMV, EBV, HPV, anti-TNF, infliximab, adalimumab, certolizumab pegol and etanercept. Hepatitis B and C and HIV had the largest literature associated and these have been summarized in Tables. RESULTS: Particular risks are associated with the use of anti-TNF drugs in patients with hepatitis B infection, in whom reactivation is common unless anti-viral prophylaxis is used. Reactivation of herpes zoster is the most common viral problem associated with anti-TNF treatment, and may be particularly severe. Primary varicella infection may present with atypical features in patients on anti-TNF. CONCLUSION: Appreciation of risks of chronic viral disease associated with anti-TNF therapy may permit early recognition, prophylaxis and treatment.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".