Golimumab effectiveness and safety in clinical practice for moderately active ulcerative colitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Golimumab (GLB) is an antitumour necrosis factor-α (anti-TNF) therapy that has shown efficacy as induction and maintenance therapy for ulcerative colitis (UC). We aimed to describe the outcome of GLB therapy for UC in a real-world clinical practice. PATIENTS AND METHODS: Consecutive patients receiving GLB for UC in six Irish Academic Medical Centres were identified. The primary study endpoint was the 6-month corticosteroid-free remission rate. The secondary endpoints included the 3-month clinical response, time free of GLB discontinuation and adverse events. RESULTS: Seventy-two patients were identified [57% men; median (range) age of 41.4 years (20.3-76.8); disease duration 6.6 years (0-29.9); follow-up 8.7 months (0.4-39.2)]. Sixty-four percent of patients were anti-TNF naive. The 3-month clinical response and the 6-month corticosteroid-free remission rates were 55 and 39%, respectively. Forty-four percent of patients discontinued GLB during the follow-up, median (95% confidence interval) time to GLB discontinuation 18.7 months (9.2-28.1). A C-reactive protein more than 5 mg/l at baseline was associated with failure to achieve 6-month corticosteroid-free remission and a shorter time to GLB discontinuation, odds ratio 0.2 (0.1-0.7), P=0.008, and hazard ratio (95% confidence interval) 2.8 (1.3-5.7), P=0.007, respectively. Adverse events occurred in 7% of patients (n=5), all of which were minor and self-limiting. CONCLUSION: These real-world clinical data suggest that GLB is an effective and safe therapy for a UC cohort with significant previous anti-TNF exposure. An elevated baseline C-reactive protein, likely reflective of increased inflammatory burden, is associated with a reduced likelihood of a successful outcome of GLB therapy.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".