Efficacy and Safety of Tofacitinib for Acute Severe Ulcerative Colitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Acute severe ulcerative colitis (ASUC) is associated with a high risk of colectomy. About 30% of patients do not respond to steroids, requiring rescue therapy. This study aims to evaluate the efficacy and safety of tofacitinib in ASUC. Methods: MEDLINE, Embase, and Cochrane Library were systematically searched. We used random-effects model to calculate pooled proportions with 95% confidence intervals (CIs). For outcomes with ≥ 2 comparative studies, we conducted pairwise meta-analyses and calculated pooled odds ratios (ORs) with 95% CIs. Results: We included six studies. Tofacitinib had a 90-day colectomy rate of 15.1% (95% CI: 8.5-25.3%). The clinical response rate at weeks 12 - 14 was 45.4% (95% CI: 32.6-58.9%), and at week 52 was 30.0% (95% CI: 17.4-46.5%). The clinical remission rate at weeks 12 - 14 was 38.1% (95% CI: 28.7-48.5%), and at week 52 was 27.1% (95% CI: 15.2-43.5%). Steroid-free clinical remission rate was 28.6% (95% CI: 22.2-36.1%) at weeks 12 - 14 and 33.1% (95% CI: 25.6-41.6%) at week 52. The most common adverse events were Clostridioides difficile infection, nausea, cardiovascular events, arthralgia or myalgia, herpes zoster infection, venous thromboembolism, and pneumonia. There was no significant difference in 90-day colectomy rate between tofacitinib and control (OR: 0.52; 95% CI: 0.25 - 1.08; P = 0.08). Conclusion: Tofacitinib demonstrated high clinical response and remission rates, and low adverse events rate. Additionally, there was a trend toward a lower 90-day colectomy rate compared to controls.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".