Adalimumab, Infliximab, and Vedolizumab in Treatment of Ulcerative Colitis: A Long-Term Retrospective Study in a Tertiary Referral Center
Bibliographic record
Abstract
Background: Biological therapies have changed the landscape of pharmacological management of ulcerative colitis (UC). However, a large proportion of patients do not respond to biologics, lose their response over time, or present adverse drug events. This study aims to assess therapeutic response and treatment persistence to adalimumab, infliximab, and vedolizumab, 3 agents widely used in a tertiary referral center of Saguenay-Lac-Saint-Jean (Quebec, Canada). Methods: We conducted a retrospective population-based study with a thorough review of patients' medical charts. Adults at UC diagnosis, with current or past use of adalimumab, infliximab, or vedolizumab, were included in the study. Clinical data were collected in order to assess response phenotypes and persistence to treatment. Kaplan-Meier curves were performed to assess treatment persistence, and predictors for discontinuation were assessed using Cox regression analyses. Results: A total of 134 patients were included in this study. For the cases exposed to adalimumab, infliximab, and vedolizumab, 56.9%, 62.5%, and 47.5% were responders, respectively. Mean persistence rates (95% CI) were 5.5 (4.3-6.6), 10.1 (8.7-11.5), and 3.6 (2.9-4.2) years for adalimumab, infliximab, and vedolizumab, respectively. Increased persistence rates were observed in biologic-naïve patients treated with infliximab in comparison to those with the previous exposition to 2 biologics, but no such effect was observed for adalimumab or vedolizumab. Overall, 61.9% of cases had adverse drug events and of these, 6 led to treatment discontinuation. Conclusion: This study presents long-term treatment persistence data with adalimumab, infliximab, and vedolizumab, showing that more than half of cases treated with these biologics remained on treatment at least 24 months after initiation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".