Infectious and Malignant Complications of TNF Inhibitor Therapy in IBD
Bibliographic record
Abstract
Tumor necrosis factor (TNF) inhibitors are being increasingly utilized in the management of inflammatory bowel disease (IBD). Although the benefits associated with TNF inhibitor therapy are undeniable, concerns have been raised about the associated risk of infectious and malignant complications. In this narrative review, we will present the evidence from studies that have evaluated the association of TNF inhibitors and both overall and specific infections and malignancy. Overall, although TNF inhibitors may increase the risk of tuberculosis, varicella, and other opportunistic infections, there is little evidence suggesting that anti-TNF agents specifically raise the overall risk of serious infections. Similarly, there is little evidence that TNF antagonists raise the risk of developing malignancy over and above the risks from concomitant therapies and the underlying disease process. However, the risk of nonmelanoma skin cancers may be increased and that is further enhanced by use of combination TNF inhibitor and thiopurine therapy. The risk of non-Hodgkin's lymphoma is statistically increased among combination therapy users. The absolute risk remains a very small but feared risk. It is difficult to fully quantify the risk of these cancers among users of TNF inhibitor therapy in the absence of concurrent thiopurine therapy. We recommend that clinicians remain mindful about the potential risks of infectious and malignant complications in their IBD patients who are using TNF inhibitors, but that further research is required to better study these risks over the long-term course of therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".