Lymphoproliferative disorders in inflammatory bowel disease patients on immunosuppression: Lessons from other inflammatory disorders
Bibliographic record
Abstract
Immunosuppressive agents, such as thiopurines, methotrexate, and biologics, have revolutionized the treatment of inflammatory bowel disease (IBD). However, a number of case reports, case control studies and retrospective studies over the last decade have identified a concerning link between immunosuppression and lymphoproliferative disorders (LPDs), the oncological phenomenon whereby lymphocytes divide uncontrollably. These LPDs have been associated with Epstein-Barr virus (EBV) infection in which the virus provides the impetus for malignant transformation while immunosuppression hampers the immune system's ability to detect and clear these malignant cells. As such, the use of immunosuppressive agents may come at the cost of increased risk of developing LPD. While little is known about the LPD risk in IBD, more is known about immunosuppression in the post-transplantation setting and the development of EBV associated post-transplantation lymphoproliferative disorders (PTLD). In review of the PTLD literature, evidence is available to demonstrate that certain immune suppressants such as cyclosporine and T-lymphocyte modulators in particular are associated with an increased risk of PTLD development. As well, high doses of immunosuppressive agents and multiple immunosuppressive agent use are also linked to increased PTLD development. Here, we discuss these findings in context of IBD and what future studies can be taken to understand and reduce the risk of EBV-associated LPD development from immunosuppression use in IBD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".