Evaluation of discontinuation for adverse events of JAK inhibitors and bDMARDs in an international collaboration of rheumatoid arthritis registers (the ‘JAK-pot’ study)
Bibliographic record
Abstract
BACKGROUND: In a clinical trial setting, patients with rheumatoid arthritis (RA) taking the Janus kinase inhibitor (JAKi) tofacitinib demonstrated higher adverse events rates compared with those taking the tumour necrosis factor inhibitors (TNFi) adalimumab or etanercept. OBJECTIVE: Compare treatment discontinuations for adverse events (AEs) among second-line therapies in an international real-world RA population. METHODS: Patients initiating JAKi, TNFi or a biological with another mode of action (OMA) from 17 registers participating in the 'JAK-pot' collaboration were included. The primary outcome was the rate of treatment discontinuation due to AEs. We used unadjusted and adjusted cause-specific Cox proportional hazard models to compare treatment discontinuations for AEs among treatment groups by class, but also evaluating separately the specific type of JAKi. RESULTS: Of the 46 913 treatment courses included, 12 523 were JAKi (43% baricitinib, 40% tofacitinib, 15% upadacitinib, 2% filgotinib), 23 391 TNFi and 10 999 OMA. The adjusted cause-specific hazard rate of treatment discontinuation for AEs was similar for TNFi versus JAKi (1.00, 95% CI 0.92 to 1.10) and higher for OMA versus JAKi (1.11, 95% CI 1.01 to 1.23), lower with TNFi compared with tofacitinib (0.81, 95% CI 0.71 to 0.90), but higher for TNFi versus baricitinib (1.15, 95% CI 1.01 to 1.30) and lower for TNFi versus JAKi in patients 65 or older with at least one cardiovascular risk factor (0.79, 95% CI 0.65 to 0.97). CONCLUSION: While JAKi overall were not associated with more treatment discontinuations for AEs, subgroup analyses suggest varying patterns with specific JAKi, such as tofacitinib, compared with TNFi. However, these observations should be interpreted cautiously, given the observational study design.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".