Efficacy and safety of tofacitinib dose de‐escalation and dose escalation for patients with ulcerative colitis: results from OCTAVE Open
Bibliographic record
Abstract
BACKGROUND: For patients with UC, flexible maintenance dosing therapy may confer advantages for safety, efficacy, costs and patient preference. Tofacitinib is an oral, small molecule JAK inhibitor for the treatment of UC. AIM: To assess the efficacy and safety of tofacitinib dose de-escalation and escalation in patients with UC. METHODS: We evaluated data (November 2017 data cut-off) from OCTAVE Open, an ongoing, open-label, long-term extension study. The dose de-escalation group comprised 66 tofacitinib induction responders in remission following 52 weeks' tofacitinib 10 mg b.d. maintenance therapy, subsequently de-escalated to 5 mg b.d. in OCTAVE Open. The dose escalation group comprised 57 tofacitinib induction responders who experienced treatment failure while receiving 5 mg b.d. maintenance therapy, subsequently escalated to 10 mg b.d. in OCTAVE Open. RESULTS: After tofacitinib de-escalation, 92.4% (61/66) and 84.1% (53/63) of patients maintained clinical response and 80.3% (53/66) and 74.6% (47/63) maintained remission, at months 2 and 12, respectively. After dose escalation, 57.9% (33/57) and 64.9% (37/57) of patients recaptured clinical response and 35.1% (20/57) and 49.1% (28/57) were in remission, at months 2 and 12, respectively. The incidence rate of herpes zoster with dose escalation (7.6 patients with events/100 patient-years) was numerically higher than in the overall tofacitinib UC programme. CONCLUSIONS: Following tofacitinib de-escalation in patients already in remission on 10 mg b.d., most maintained remission, although 25.4% lost remission, at month 12. For induction responders who dose-escalated following treatment failure on 5 mg b.d. maintenance therapy, 49.1% achieved remission by month 12. (ClinicalTrials.gov number: NCT01470612).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".