P0602 Long-term effectiveness and safety of ustekinumab dose escalation in patients with refractory ulcerative colitis: a multicenter retrospective cohort study
Bibliographic record
Abstract
Abstract Background Ustekinumab dose escalation (DE) may be an effective strategy to recapture clinical response in patients with ulcerative colitis (UC). The aim of this study was to assess the real-world long-term effectiveness and safety of ustekinumab DE in patients with refractory UC. Methods This multicenter retrospective cohort study included patients with refractory UC who received at least one IV induction ustekinumab dose between January 2016 and November 2021. We compared ustekinumab DE to no DE, examining clinical, biochemical, and endoscopic disease outcomes. The primary endpoint was corticosteroid-free clinical remission (partial Mayo score ≤ 2 without systemic corticosteroids) at the end of follow-up. Cox-proportional hazards regression analysis was performed for factors associated with time to DE, and a Kaplan-Meier plot was created for visualizing drug persistence probabilities. Results We enrolled 121 patients. Eighty-one patients (67%) underwent DE during a median follow-up of 141 weeks. Corticosteroid-free clinical remission at the end of follow-up was achieved for 53.2% (DE group) and 59.0% (non-DE group). In the DE group, 53% discontinued ustekinumab, mainly due to a lack of effectiveness. At the end of follow-up, 47% of DE patients remained on ustekinumab, compared to 55% in the non-DE group. Ustekinumab persistence probability after 2 years was 40% (DE group) versus 79% (non-DE group). Only 2 patients discontinued ustekinumab for adverse events. Conclusion Our results indicate that DE is a common method for optimizing ustekinumab treatment in refractory UC. While DE appears safe, effectiveness and drug persistence of DE beyond 2 years are limited.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".