Infections in patients with rheumatoid arthritis receiving tofacitinib versus tumour necrosis factor inhibitors: results from the open-label, randomised controlled ORAL Surveillance trial
Bibliographic record
Abstract
OBJECTIVES: To characterise infections in patients with rheumatoid arthritis (RA) in ORAL Surveillance. METHODS: In this open-label, randomised controlled trial, patients with RA aged≥50 years with ≥1 additional cardiovascular risk factor received tofacitinib 5 or 10 mg two times per day or a tumour necrosis factor inhibitor (TNFi). Incidence rates (IRs; patients with first events/100 patient-years) and hazard ratios (HRs) were calculated for infections, overall and by age (50-<65 years; ≥65 years). Probabilities of infections were obtained (Kaplan-Meier estimates). Cox modelling identified infection risk factors. RESULTS: IRs/HRs for all infections, serious infection events (SIEs) and non-serious infections (NSIs) were higher with tofacitinib (10>5 mg two times per day) versus TNFi. For SIEs, HR (95% CI) for tofacitinib 5 and 10 mg two times per day versus TNFi, respectively, were 1.17 (0.92 to 1.50) and 1.48 (1.17 to 1.87). Increased IRs/HRs for all infections and SIEs with tofacitinib 10 mg two times per day versus TNFi were more pronounced in patients aged≥65 vs 50-<65 years. SIE probability increased from month 18 and before month 6 with tofacitinib 5 and 10 mg two times per day versus TNFi, respectively. NSI probability increased before month 6 with both tofacitinib doses versus TNFi. Across treatments, the most predictive risk factors for SIEs were increasing age, baseline opioid use, history of chronic lung disease and time-dependent oral corticosteroid use, and, for NSIs, female sex, history of chronic lung disease/infections, past smoking and time-dependent Disease Activity Score in 28 joints, C-reactive protein. CONCLUSIONS: Infections were higher with tofacitinib versus TNFi. Findings may inform future treatment decisions. TRIAL REGISTRATION NUMBER: NCT02092467.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".