Risk of diverticulitis and gastrointestinal perforation in rheumatoid arthritis treated with tocilizumab compared to rituximab or abatacept
Bibliographic record
Abstract
OBJECTIVE: To compare the risk of diverticulitis and gastrointestinal perforation (GIP) in RA treated with tocilizumab (TCZ) compared with rituximab (RTX) and abatacept (ABA). METHODS: We conducted a population-based study using three observational French registries on TCZ, RTX and ABA in RA. Using a propensity score approach, we compared the risk of diverticulitis or GIP in these patients. RESULTS: With inverse probability weighting, there was an increased risk of diverticulitis in TCZ-treated patients compared with RTX- or ABA-treated patients [hazard ratio (HR)=3.1 (95% CI: 1.5, 6.3), P =0.002]. Moreover, patients treated with TCZ had also an increased risk of GIP due to diverticulitis compared with those treated with RTX or ABA [HR=3.8 (1.1-13.6), P =0.04], resulting in an overall increased risk of GIP [HR=2.9 (1.1-7.8), P =0.03], while no significant increased risk of GIP due to any other aetiology was found in TCZ treated patients. Diverticulitis and GIP occurred earlier with TCZ than other drugs after the last perfusion (P =0.01), with atypical clinical presentation (slow transit in 30%, P =0.04) and lower acute-phase reactants at the time of the event (P =0.005). CONCLUSION: TCZ for RA was associated with increased odds of diverticulitis as well as GIP due to diverticulitis as compared with RTX and ABA. Our study confirms the increased odds of GIP in patients receiving TCZ, which might be explained by an increased risk of diverticulitis with misleading clinical presentation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".