P51 Benefit-risk of upadacitinib in patients with moderately to severe ulcerative colitis (UC)
Bibliographic record
Abstract
<h3>Introduction</h3> Here, key safety and efficacy data from the maintenance study of Upadacitinib (UPA) phase 3 clinical trial in UC are summarized. <h3>Aims and Methods</h3> Patients with a clinical response (per Adapted Mayo score) after 8 weeks induction with UPA 45 mg OD were re-randomized in maintenance to receive UPA 15 mg OD, UPA 30 mg OD, or placebo (PBO). For efficacy outcomes, point estimates and 95% confidence intervals (CI) of the PBO-adjusted treatment effect were calculated. For the risk analysis, exposure-adjusted event rates (events per 100 patient-years [E/100 PY]) were evaluated. <h3>Results</h3> Primary endpoint of clinical remission at Week 52 were 30.1% (95% CI: 22.7, 37.4) with UPA 15 mg and 42.9% (35.4, 50.4) with UPA 30 mg (p<0.001 for both; <b>table 1</b>). All secondary endpoints were significantly different. Serious infections were 5.9 E/100 PY with PBO vs 5.0 and 3.2 with UPA 15 mg and30 mg, respectively. Herpes Zoster with UPA 15 mg and 30 mg were 6.0 and 7.3 E/100 PY, respectively versus 0 in placebo. Malignancy excluding non-melanoma skin cancer were 0.7 E/100 PY with PBO vs 0.5 and 0.9 with UPA 15 mg and 30 mg, respectively; non-melanoma skin cancer were 1.4 E/100 PY with UPA 30 mg, with no cases with UPA 15 mg or PBO. Venous thromboembolic events were low in the UPA groups with no cases reported in PBO (<b>table 1</b>). <h3>Conclusion</h3> Response rates were significantly greater with UPA 15 mg and 30 mg versus PBO across all endpoints. Generally, both UPA doses were well tolerated and have a favorable benefit-risk profile after 52 weeks’ maintenance. The safety of UPA will continue to be monitored.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".