Monitoring of Vedolizumab Infusion Therapy (MOVE-IT) Response With Fecal Inflammation Markers, Ultrasound, and Trough Serum Level in Patients With Ulcerative Colitis: Protocol for a Multicentric, Prospective, Noninterventional Study
Bibliographic record
Abstract
BACKGROUND: Vedolizumab has been shown to induce clinical remission in patients with active ulcerative colitis. Treatment with anti-integrin vedolizumab leads to clinical remission in 16.9% and clinical response in 47.1% of cases after 6 weeks. However, in clinical practice, no decision to discontinue or continue vedolizumab therapy is made until 14 weeks at the earliest. OBJECTIVE: The aim of this study is to develop an algorithm for optimizing vedolizumab administration in patients with moderate-to-severe ulcerative colitis by calculating the probability of clinical response at week 14, on the basis of the data from week 6. METHODS: This is a prospective, single-arm, multicentric, noninterventional, observational study with no interim analyses and a sample size of 35 evaluable patients. RESULTS: The enrollment started in August 2018 and was still open at the date of submission. The study is expected to complete in September 2020. CONCLUSIONS: The early identification of patients who are responding to an integrin antibody is therapeutically beneficial. At the same time, patients who are not responding can be identified earlier. The development of a therapeutic algorithm for identifying patients as responders or nonresponders can thus help prescribing physicians avoid ineffective treatments and stop these very early.
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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 | 0.049 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".