Antitumor Necrosis Factor-α Therapy Associated with Inflammatory Bowel Disease: Three Cases and a Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: Antitumor necrosis factor-α (anti-TNF-α) therapy is the most prescribed biologic agent therapy in rheumatology and gastroenterology. However, a number of serious side effects have been reported with these drugs. Only a handful of cases of new-onset inflammatory bowel disease (IBD), mostly in children diagnosed with juvenile idiopathic arthritis (JIA), have been reported during anti-TNF-α therapy. We present 3 cases of adult IBD following anti-TNF-α therapy and a literature review on this topic. METHODS: We searched PubMed MESH for all relevant terms, papers were reviewed, and patient-specific data were extracted. Relevant clinical data were calculated and presented. RESULTS: The PubMed search resulted in 137 articles, of which 11 articles and 4 cited publications were included in our analysis. We found 53 cases of IBD after anti-TNF-α therapy reported in the literature; most of them were case series collected retrospectively from national databases or studies. Almost all the patients developed IBD after the introduction of etanercept (ETN); 2 patients with rheumatoid arthritis were also included. The average age at IBD onset was 17.3 years and the average time from ETN introduction to IBD onset was 27 months (± 24). Gastrointestinal symptoms have been reported as improving or subsiding in most of the patients after discontinuing ETN. CONCLUSION: Although this manifestation is not common, it should be taken into consideration as an adverse effect of ETN. Rheumatologists, and in particular rheumatologists treating adult patients, should be aware of this possible complication. Further investigation about the pathogenic process underlying this phenomenon is warranted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".