Impact of concomitant corticosteroid use on adverse events in ulcerative colitis clinical trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: Adverse events (AEs) are frequently reported in clinical trials of advanced therapies for ulcerative colitis (UC). It remains uncertain whether patients receiving concomitant corticosteroids experience higher AE rates. This study aimed to determine whether corticosteroid use is associated with increased AEs in UC trials. METHODS: This post-hoc analysis used participant-level data from several placebo-controlled trials (GEMINI-1, ULTRA-2, VARSITY, ACT-1, OCTAVE, and PURSUIT). The primary population included induction responders with or without baseline corticosteroid use. The primary outcome assessed the association between corticosteroid use and total AEs. Secondary outcomes included AE rates among those who achieved corticosteroid-free remission versus those who did not, and whether specific AEs were more common in corticosteroid users. RESULTS: Among 2339 patients who achieved clinical response after induction, 1159 (49.5%) used corticosteroids at baseline. By 1 year, AE incidence was higher among corticosteroid users than non-users (75.2% vs. 67.6%, P < .001). Patients who achieved corticosteroid-free remission had fewer AEs than those who did not (67.4% vs. 77.5%, P < .001). Moderate AEs were more frequent among corticosteroid users. Common AEs included infections (in both groups) and liver abnormalities (more in corticosteroid users). Multivariable logistic regression confirmed baseline corticosteroid use as an independent AE risk factor [odds ratio (OR) 1.5, 95% CI 1.3-1.8, P = .002]. Ongoing corticosteroid use at 1 year was associated with even higher AE risk (OR 4.6, 95% CI 2.1-6.8, P < .001). CONCLUSION: Corticosteroid use is independently associated with increased AEs in UC clinical trials. Continued monitoring and strategies for corticosteroid tapering may reduce AE burden.
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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 | 0.112 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".