The effectiveness of second- and-third-line biologics in perianal Crohn’s disease—a multicenter propensity score-matched study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Anti-tumor necrosis factor-α inhibitors (anti-TNFs) are the established treatment for perianal Crohn's disease (pCD), but relapse and non-response are common. Data on second- and third-line biologics are limited. We present the first direct comparison of second- and third-line biologics in pCD patients with active perianal disease previously treated with first-line anti-TNFs. METHODS: A multicenter retrospective cohort study included adult patients with pCD who failed first-line anti-TNF. The primary outcome was clinical perianal response, with secondary outcomes of radiological response (magnetic resonance imaging or transrectal ultrasound) and healing, and clinical remission. Propensity score matching (PSM) was used to adjust for baseline differences. RESULTS: A total of 486 pCD patients from 23 IBD centers were included, with 333/486 (68.5%) and 216/263 (82.1%) matched by PSM in the second and third-line treatment groups, respectively. In the second-line group, 62/78 (79.5%) of ustekinumab (UST)-treated patients achieved clinical perianal response, compared to 46/78 (58.9%) with vedolizumab (VDZ) (OR 4.47, 95% CI, 1.94-10.28, P < .001) and 38/78 (48.7%) with anti-TNFs (OR 5.29, 95% CI, 2.39-11.71, P < .001). In the third-line group, 38/49 (77.6%) of UST-treated patients achieved clinical perianal response, compared to 29/49 (59.2%) with VDZ (OR 9.96, 95% CI, 2.6-38.4, P < .001) and 27/49 (55.1%) with anti-TNFs (OR 12.03, 95% CI, 2.99-48.47, P < .001). UST-treated patients also had higher radiological response rates than VDZ (OR 3.28, 95% CI, 1.07-10.07, P = .038). CONCLUSION: In pCD patients failing anti-TNFs as first-line treatment, ustekinumab may be more effective than vedolizumab or another anti-TNF as second or third-line 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".