Effectiveness of tofacitinib in patients with ulcerative colitis: an updated systematic review and meta-analysis of real-world studies
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the real-world effectiveness of tofacitinib for treating moderate-to-severe ulcerative colitis (UC). DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, EMBASE and Cochrane CENTRAL databases were searched from inception up to 18 July 2023. Reference lists of included studies were manually searched to identify potentially relevant studies not found in the databases. ELIGIBILITY CRITERIA: Eligible studies included real-world observational studies, reported in English, on patients with moderate-to-severe UC treated with tofacitinib, defined by the Partial Mayo Score. Excluded were clinical trials, reviews, letters, conference abstracts, case reports and studies involving patients with mixed Crohn's disease. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers extracted data and recorded it in Excel. Quality assessment was performed using the Newcastle-Ottawa scale. Meta-analysis was performed using random-effects models due to high heterogeneity across studies. RESULTS: 19 studies containing a total of 2612 patients were included. Meta-analysis revealed that clinical response rates were 58% at week 8, 61% at weeks 12-16, 51% at weeks 24-26 and 51% at week 52. Clinical remission rates were 39% at week 8, 43% at weeks 12-16, 40% at weeks 24-26 and 43% at week 52. Corticosteroid-free clinical remission rates were 33% at week 8, 37% at weeks 12-16, 32% at weeks 24-26 and 40% at week 52. CONCLUSION: This meta-analysis of real-world studies indicates that treatment of UC with tofacitinib is associated with favourable clinical response and remission rates in the induction and maintenance phases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".