Effectiveness of Third-Class Biologic Treatment in Crohn’s Disease: A Multi-Center Retrospective Cohort Study
Bibliographic record
Abstract
Background: Multiple studies have described the effectiveness of ustekinumab (UST) and vedolizumab (VDZ) in patients with Crohn’s disease (CD) failing anti- Tumor necrosis factors (TNFs); however, the effectiveness of VDZ or UST as a third-class biologic has not yet been described. Aims and Methods: In this retrospective multicenter cohort study, we aimed to investigate the effectiveness of VDZ and UST as a third-class biologic in patients with CD. Results: Two-hundred and four patients were included; 156/204 (76%) patients received VDZ as a second- and UST as a third-class therapy (group A); the remaining 48/204 (24%) patients received UST as a second- and VDZ as a third-class therapy (group B). At week 16–22, 87/156 (55.5%) patients and 27/48 (56.2%) in groups A and B, respectively, responded to treatment (p = 0.9); 41/156 (26.2%) and 15/48 (31.2%) were in clinical remission (p = 0.5). At week 52; 89/103 (86%) patients and 25/29 (86.2%) of the patients with available data had responded to third-class treatment in groups A and B, respectively (p = 0.9); 31/103 (30%) and 47/29 (24.1%) were in clinical remission (p = 0.5). Conclusion: Third-class biological therapy was effective in more than half of the patients with CD. No differences in effectiveness were detected between the use of VDZ and UST as a third-class agent.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".