REMIT-UC: Real-World Effectiveness and Safety of Tofacitinib for Moderate-to-Severely Active Ulcerative Colitis
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate the real-world effectiveness and safety of tofacitinib for the treatment of ulcerative colitis (UC). METHODS: REMIT-UC is a Canadian multicenter cohort study. Standardized data collection was performed on 334 consecutive adult outpatients with UC treated with tofacitinib. The primary outcomes were achievement of clinical and endoscopic remission. Safety outcomes were reported using incidence rates (events/100 patient-years of exposure). A multivariable Cox proportional hazards model was used to evaluate predictors of loss of response after tofacitinib dose de-escalation to 5 mg twice daily (BID). RESULTS: Clinical remission was achieved by 35.3% (106/300), 36.0% (104/289), and 35.2% (93/264) of patients at weeks 12, 24, and 52, respectively. Endoscopic remission was achieved by 18.5% (15/81), 23.0% (28/122), and 25.7% (35/136) of patients at weeks 12, 24, and 52, respectively. Incidence of serious infections, herpes zoster, and venous thromboembolism were 2.1 [0.9-4.2], 0.5 [0.1-1.9], and 1.1 [0.3-2.7], respectively. Among responders, 44.5% (109/245) lost response during follow-up, which was recaptured in 54.9% (39/71) of patients who re-escalated to 10 mg BID. Patients with a baseline Mayo endoscopic score of 3 (adjusted hazard ratio 3.60 [95% confidence interval: 1.70-7.62]) and prior biologic failure (adjusted hazard ratio 3.89 [95% confidence interval: 1.28-11.86]) were at a higher risk for losing response after dose reduction. DISCUSSION: One-third of patients with UC treated with tofacitinib achieved clinical remission with few serious adverse events. However, half of patients lost response with de-escalation, which was only partially recaptured with increasing the maintenance dose. Those with negative prognostic factors should be counselled about the risks and benefits of continuing high doses of tofacitinib.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".