Real‐world experience of switching from intravenous to subcutaneous vedolizumab maintenance treatment for inflammatory bowel diseases
Bibliographic record
Abstract
BACKGROUND: Subcutaneous (SC) vedolizumab is effective in inflammatory bowel diseases (IBD) when administered after induction with two infusions. AIM: To assess the effectiveness, safety and pharmacokinetics of a switch from intravenous (IV) to SC maintenance vedolizumab in patients with IBD METHODS: In this prospective cohort study, patients with IBD who had ≥4 months IV vedolizumab were switched to SC vedolizumab. We studied the time to discontinuation of SC vedolizumab, adverse events (AEs), changes in clinical and biochemical outcomes and vedolizumab concentrations at baseline, and weeks 12 and 24. RESULTS: We included 82 patients with Crohn's disease (CD) and 53 with ulcerative colitis (UC). Eleven (13.4%) patients with CD and five (9.4%) with UC discontinued SC vedolizumab after a median of 18 (IQR 8-22) and 6 weeks (IQR 5-10), respectively. Four patients with CD switched to a different drug due to loss of response, nine switched back to IV vedolizumab due to adverse events, and three due to needle fear. Common AEs were injection site reactions (n = 15) and headache (n = 6). Median clinical and biochemical disease activity remained stable after the switch. Median serum vedolizumab concentrations increased from 19 μg/ml at the time of the switch to 31 μg/ml 12 weeks after the switch (p < 0.005). CONCLUSIONS: Switching from IV to SC vedolizumab maintenance treatment is effective in patients with CD or UC. However, 9% of patients were switched back to IV vedolizumab due to adverse events or fear of needles.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".