Persistence of treatment in patients with ulcerative colitis who responded to tofacitinib therapy: data from the open‐label, long‐term extension study, <scp>OCTAVE</scp> open
Bibliographic record
Abstract
Summary Background Tofacitinib is an oral Janus kinase inhibitor for the treatment of ulcerative colitis (UC). Aim This post hoc analysis evaluated tofacitinib persistence in patients with UC in OCTAVE Open, an open‐label, long‐term extension study of patients receiving tofacitinib 5 or 10 mg twice daily. Methods Kaplan‐Meier estimates for tofacitinib drug survival and reasons for discontinuations were evaluated. Baseline factors were analysed as predictors of persistence. Results This analysis included 603 patients: 280 entered OCTAVE Open with a clinical response (164 in remission and 116 not in remission), 220 were delayed responders, 75 were retreatment responders and 35 were dose escalation responders, treated for up to 7 years in OCTAVE Open. Of these, 118 (42.1%) responders, 121 (55.0%) delayed responders, 40 (53.3%) retreatment responders and 17 (48.6%) dose escalation responders discontinued tofacitinib with a median time to discontinuation of 5.6, 4.5, 4.0 and 4.4 years, respectively. The estimated 2‐ and 5‐year drug survival rates in the responders (including patients in remission and not in remission) were 73.9% and 54.5%, respectively. Corresponding persistence values for delayed responders were 69.5% and 45.2%, for retreatment responders, 70.7% and 40.0%, and for dose escalation responders, 74.3% and 32.8%. Conclusion In OCTAVE Open, a high proportion of patients maintained tofacitinib treatment, with the median survival by group ranging from 4.0 to 5.6 years although these analyses are post hoc and limited by sample size. Further research should focus on factors to enhance persistence with tofacitinib treatment in patients with UC.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".