Systematic review with meta‐analysis: comparative risk of lymphoma with anti‐tumour necrosis factor agents and/or thiopurines in patients with inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: The risk of lymphoma in patients with inflammatory bowel disease (IBD) treated with anti-TNF agents remains unclear. AIM: To assess the comparative risk of lymphoma with anti-TNF agents and/or thiopurines in IBD METHODS: We searched PubMed, EMBASE and Cochrane Library to identify studies that evaluated lymphoproliferative disorders associated with anti-TNF agents with or without thiopurines. The risk of lymphoma was assessed through four comparator groups: combination therapy (anti-TNF plus thiopurine), anti-TNF monotherapy, thiopurine monotherapy and control group. Pooled incidence rate ratios (IRR) were estimated through Poisson-normal models. RESULTS: Four observational studies comprising 261 689 patients were included. As compared with patients unexposed to anti-TNF and thiopurines, those exposed to anti-TNF monotherapy, thiopurine monotherapy or combination therapy had pooled IRR (per 1000 patient-years) of lymphoma of 1.52 (95% CI: 1.06-2.19; P = 0.023), 2.23 (95% CI: 1.79-2.79; P < 0.001) and 3.71 (95% CI: 2.30-6.00; P ≤ 0.01), respectively. The risk of lymphoma associated with combination therapy was higher than with thiopurines or anti-TNF alone with pooled IRR of 1.70 (95% CI: 1.03-2.81; P = 0.039) and 2.49 (95% CI: 1.39-4.47; P = 0.002), respectively. The risk did not differ between anti-TNF monotherapy and thiopurine monotherapy with pooled IRR of 0.72 (95% CI: 0.48-1.07; P = 0.107). All observational studies were of high quality according to the Newcastle-Ottawa scale. CONCLUSIONS: There is an increased risk of lymphoma in IBD patients treated with anti-TNF agents, either alone or when combined with thiopurines.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".