Tofacitinib for the Treatment of Ulcerative Colitis: Analysis of Infection Rates from the Ulcerative Colitis Clinical Programme
Bibliographic record
Abstract
BACKGROUND AND AIMS: Tofacitinib is an oral, small molecule JAK inhibitor for the treatment of ulcerative colitis. We report integrated analyses of infections in the Phase [P]2 and P3 OCTAVE programmes. METHODS: Three cohorts were analysed: Induction [P2/3 induction studies]; Maintenance [P3 maintenance study]; and Overall [all tofacitinib-treated patients in induction, maintenance, or ongoing, open-label, long-term extension studies; as of May 2019]. Proportions and incidence rates [IRs; unique patients with events/100 patient-years] of serious infections [SIs], herpes zoster [HZ] [non-serious and serious], and opportunistic infections [OIs] are reported [censored at time of event]. RESULTS: In the Induction Cohort [N = 1220], no patients receiving placebo and eight [0.9%] receiving tofacitinib 10 mg twice daily [BID] developed SIs. Maintenance Cohort [N = 592] SI IRs (95% confidence interval [CI]) were 1.94 [0.23-7.00] for placebo and 1.35 [0.16-4.87] and 0.64 [0.02-3.54] for tofacitinib 5 and 10 mg BID, respectively; HZ IRs were 0.97 [0.02-5.42], 2.05 [0.42-6.00], and 6.64 [3.19-12.22], respectively. In the Overall Cohort [N = 1157; 82.9% predominantly received tofacitinib 10 mg BID], SI, HZ, and non-HZ OI IRs were 1.70 [1.24-2.27], 3.48 [2.79-4.30], and 0.15 [0.04-0.38], respectively. No SIs resulted in death. CONCLUSIONS: During induction, SIs were more frequent with tofacitinib versus placebo. SIs were generally infrequent in the Maintenance and Overall Cohorts, with rates comparable between treatment groups. Maintenance Cohort HZ IR was numerically higher with tofacitinib 10 mg BID versus 5 mg BID. Overall Cohort HZ IRs remained stable over time. Non-HZ OIs and viral infections were rare.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".