Real-Life Efficacy of Tofacitinib in Various Situations in Ulcerative Colitis: A Retrospective Worldwide Multicenter Collaborative Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Tofacitinib (TFB) appears to be effective in the treatment of ulcerative colitis (UC); however, available real-world studies are limited by cohort size. TFB could be an option in the treatment of acute severe ulcerative colitis (ASUC). We aimed to investigate efficacy and safety of TFB in moderate-to-severe colitis and ASUC. METHODS: This retrospective, international cohort study enrolling UC patients with ≥6-week follow-up period was conducted from February 1 to July 31, 2022. Indications were categorized as ASUC and chronic activity (CA). Baseline demographic and clinical data were obtained. Steroid-free remission (SFR), colectomy, and safety data were analyzed. RESULTS: A total of 391 UC patients (median age 38 [interquartile range, 28-47] years; follow-up period 26 [interquartile range, 14-52] weeks) were included. A total of 27.1% received TFB in ASUC. SFR rates were 23.7% (ASUC: 26.0%, CA: 22.8%) at week 12 and 41.1% (ASUC: 34.2%, CA: 43.5%) at week 52. The baseline partial Mayo score (odds ratio [OR], 0.850; P = .006) was negatively associated with week 12 SFR, while biologic-naïve patients (OR, 2.078; P = .04) more likely achieved week 52 SFR. The colectomy rate at week 52 was higher in ASUC group (17.6% vs 5.7%; P < .001) and decreased with age (OR, 0.94; P = .013). A total of 67 adverse events were reported, and 17.9% resulted in cessation of TFB. One case of thromboembolic event was reported. CONCLUSIONS: TFB is effective in both studied indications. TFB treatment resulted in high rates of SFR in the short and long terms. Higher baseline disease activity and previous biological therapies decreased efficacy. No new adverse event signals were found.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".