Tofacitinib Versus Vedolizumab Among Bio-naive Patients With Ulcerative Colitis: A Real-World Propensity-Weighted Comparison
Bibliographic record
Abstract
BACKGROUND AND AIMS: Over the last decade, treatment options for moderate-to-severe ulcerative colitis (UC) have expanded. However, comparative studies between these agents are limited, especially among biologic-naive patients. We aimed to compare the persistence, effectiveness, and safety of tofacitinib and vedolizumab as the first advanced treatment for patients with UC. METHODS: Patients who received either tofacitinib or vedolizumab as their first advanced therapy for UC in NHS Lothian were included. We used inverse probability of treatment weighting. The probability of treatment assignment was calculated via logistic regression using age, sex, UC duration, Montreal extent, C-reactive protein, concomitant corticosteroids, and partial Mayo score at drug commencement. RESULTS: We included n = 158 patients, of whom n = 81 (51.3%) received vedolizumab and n = 77 (48.7%) tofacitinib. Median follow-up for vedolizumab patients was 3.1 years (interquartile range [IQR] 1.6-4.8) and for tofacitinib patients 1.5 years (IQR 0.3-2.3). The cohort was 59.5% male with a median age of 41.1 years (IQR 31.5-51.8). At 2 years, vedolizumab persistence was superior to tofacitinib (p = 0.005). At Weeks 12 and 52, clinical, biochemical, and fecal biomarker steroid-free remission were comparable between groups. Primary nonresponse and secondary loss of response were 9.9% and 17.3% for vedolizumab and 23.4% and 13% for tofacitinib, respectively. The frequency of adverse events was comparable (11 [13.6%] vedolizumab vs 19 [24.7%] tofacitinib, p = 0.629). CONCLUSIONS: We found that the persistence and tolerability of vedolizumab were superior to tofacitinib in bio-naive UC, although the rates of clinical and biomarker remission were comparable. These data may help inform the positioning of advanced therapy.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".