Real World Effectiveness of Golimumab Therapy in Ulcerative Colitis Regardless of Prior TNF Exposure
Bibliographic record
Abstract
BACKGROUND: The efficacy of golimumab to induce and maintain remission in biologic-naïve patients with ulcerative colitis (UC) is established from placebo-controlled trials. However, golimumab's real-world effectiveness, important to physicians and payers, remains unexplored. AIM: The goal of this study was to describe real-world use and rate of persistence among UC patients with golimumab therapy and to assess factors that predict discontinuation during golimumab maintenance treatment. METHODS: A retrospective study of UC patients receiving golimumab maintenance therapy (August 2012-August 2015) was conducted on dosing data from a national case management program. Treatment persistence, defined as time from index prescription to the last dose (gap in dose >60 days), was assessed using Kaplan-Meier survival analysis. Predictors of treatment persistence were explored with Cox proportional hazards regression. RESULTS: One hundred thirty-six patients (50.7% male) with a mean (SD) age of 44.4 (15.6) years were included. At golimumab initiation, 72.1% were naïve to anti-TNFs; 77.2% received 200 mg, while 4.4% and 18.4% received 50 mg and 100 mg, respectively, every 4 weeks (induction therapy). The median time to discontinuation was 530 days, with a cumulative probability of 63% to remain on therapy at one year. Age, gender, golimumab induction, golimumab maintenance dose and prior anti-TNF exposure were not significantly associated with treatment persistence. Dose adjustment occurred in 7.4% of patients during maintenance treatment. CONCLUSIONS: Overall, the persistence rate of golimumab observed in the current real-world study is similar to that described in previous single-centre UC cohorts and consistent with that seen in controlled clinical trials.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".