Vedolizumab induction therapy for inflammatory bowel disease in clinical practice – a nationwide consecutive German cohort study
Bibliographic record
Abstract
BACKGROUND: Vedolizumab (VDZ) is a humanised monoclonal IgG1 antibody targeting α4 β7 integrin. AIM: To investigate the real-world efficacy of vedolizumab for the treatment of Crohn's disease (CD) and ulcerative colitis (UC). METHODS: A consecutive cohort of 212 adult IBD patients with active disease (HBI >7/partial Mayo >4) newly receiving VDZ was prospectively recruited from 7 academic and 17 community centres. The primary endpoint was clinical remission (CRM) (CD HBI ≤4, UC pMayo ≤1) in week 14. Secondary endpoints included steroid-free remission (SFCRM), clinical response (CRS) (HBI/pMayo score drop ≥3), vedolizumab impact on CRP, calprotectin and haemoglobin. RESULTS: Data of 97 CD (71.1% female, HBI 11) and 115 UC (42.6% female, pMayo 6) patients were analysed. Only 5.2% CD and 24.3% UC were anti-TNFα naïve. Most had extensive mucosal involvement (Montreal L3 69.1%/E3 53.9%). At week 14, 23.7% vs. 23.5% of CD vs. UC patients achieved CRM, 19.6% vs. 19.1% SFCRM and 60.8% vs. 57.4% CRS, respectively (all based on NRI). Week 14 CRM in CD was significantly associated with no history of extraintestinal manifestations (P = 0.019), no prior adalimumab use (P = 0.011), no hospitalisation in the past 12 months (P = 0.015) and low HBI score (P = 0.02) and in UC with active or previous smoking (P = 0.044/0.028) and no anti-TNFα (P = 0.023) use. Low HBI (P = 0.019) and no hospitalisation in the past 12 months (P = 0.01) predict CD CRM. The three most common AE were joint pain, acne and nasopharyngitis. CONCLUSION: Vedolizumab is effective in routine use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".