Vedolizumab and Anti-Tumour Necrosis Factor α Real-World Outcomes in Biologic-Naïve Inflammatory Bowel Disease Patients: Results from the EVOLVE Study
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Bibliographic record
Abstract
BACKGROUND AND AIMS: This study aimed to compare real-world clinical effectiveness and safety of vedolizumab, an α4β7-integrin inhibitor, and anti-tumour necrosis factor-α [anti-TNFα] agents in biologic-naïve ulcerative colitis [UC] and Crohn's disease [CD] patients. METHODS: This was a 24-month retrospective medical chart study in adult UC and CD patients treated with vedolizumab or anti-TNFα in Canada, Greece and the USA. Inverse probability weighting was used to account for differences between groups. Primary outcomes were cumulative rates of clinical effectiveness [clinical response, clinical remission, mucosal healing] and incidence rates of serious adverse events [SAEs] and serious infections [SIs]. Secondary outcomes included cumulative rates of treatment persistence [patients who did not discontinue index treatment during follow-up] and dose escalation and incidence rates of disease exacerbations and disease-related surgeries. Adjusted analyses were performed using inverse probability weighting. RESULTS: A total of 1095 patients [604 UC, 491 CD] were included. By 24 months, rates of clinical effectiveness were similar between groups, but incidence rates of SAEs (hazard ratio [HR] = 0.42 [0.28-0.62]) and SIs (HR = 0.40 [0.19-0.85]) were significantly lower in vedolizumab vs anti-TNFα patients. Rates of treatment persistence [p < 0.01] by 24 months were higher in vedolizumab patients with UC. Incidence rates of disease exacerbations were lower in vedolizumab patients with UC (HR = 0.58 [0.45-0.76]). Other outcomes did not significantly differ between groups. CONCLUSION: In this real-world setting, first-line biologic therapy in biologic-naïve patients with UC and CD demonstrated that vedolizumab and anti-TNFα treatments were equally effective at controlling disease symptoms, but vedolizumab has a more favourable safety profile.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it