Safety of Tofacitinib in the Treatment of Rheumatoid Arthritis in Latin America Compared With the Rest of the World Population
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is a chronic, autoimmune disease characterized by joint destruction. Tofacitinib is an oral Janus kinase inhibitor for the treatment of RA. This post hoc analysis assessed the safety of tofacitinib in Latin American (LA) patients with RA versus the Rest of World (RoW) population. METHODS: Data were pooled from 14 clinical studies of tofacitinib: six Phase 2, six Phase 3 and two long-term extension studies. Incidence rates (IRs; patients with events/100 patient-years of treatment exposure) were calculated for safety events of special interest combined across tofacitinib doses. 95% confidence intervals (CI) for IRs were calculated using the maximum likelihood method. Descriptive comparisons were made between LA and RoW (excluding LA) populations. RESULTS: This analysis included data from 984 LA patients and 4687 RoW patients. IRs for safety events of special interest were generally similar between LA and RoW populations, with overlapping 95% CIs. IRs for discontinuation due to adverse events, serious infections, tuberculosis, all herpes zoster (HZ), serious HZ, malignancies (excluding non-melanoma skin cancer) and major adverse cardiovascular events were numerically lower for LA versus RoW patients; IR for mortality was numerically higher. No lymphoma was reported in the LA population versus eight cases in the RoW population. Exposure (extent and length) was lower in the LA population (2148.33 patient-years [mean = 2.18 years]) versus RoW (10515.68 patient-years [mean = 2.24 years]). CONCLUSION: This analysis of pooled data from clinical studies of tofacitinib in patients with RA demonstrates that tofacitinib has a consistent safety profile across LA and RoW patient populations.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".