Lymphoma risk in inflammatory bowel disease: Is it the disease or its treatment?
Bibliographic record
Abstract
With the increasingly widespread use of immunosuppressive and biologic agents for the treatment of Crohn's disease and ulcerative colitis come concerns about potential long-term consequences of such therapies. Disentangling the potential confounding effects of the underlying disease, its extent, severity, duration, and behavior, and concomitant medical therapy has proven to be exceedingly difficult. Unlike the case in rheumatoid arthritis, the overwhelming preponderance of population-based evidence suggests that a diagnosis of inflammatory bowel disease (IBD) is not associated with an increased relative risk of lymphoma. However, well-designed studies that evaluate the potential modifying effect of IBD severity have yet to be performed. Although the results from hospital- and population-based studies have conflicted, the results of a recent meta-analysis suggest that patients receiving purine analogs for the treatment of IBD have a lymphoma risk approximately 4-fold higher than expected. Analyses of lymphoma risk in patients receiving biologic agents directed against tumor necrosis factor-alpha are confounded by concomitant use of immunosuppressive agents in most of these patients. Nevertheless, there may be a small but real risk of lymphoma associated with these therapies. Although the relative risk of lymphoma may be elevated in association with some of the medical therapies used in the treatment of IBD, this absolute risk is low. Weighing the potential risk of lymphoma associated with select medical therapies against the risk of undertreating IBD will help physicians and patients to make more informed decisions pertaining to the medical management of 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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".