Fecal loss of vedolizumab is associated with ulcerative colitis severity, lower serum vedolizumab levels, and rates of clinical response: results from the FAVOUR study
Bibliographic record
Abstract
BACKGROUND AND AIMS: We conducted a prospective study (FAVOUR) of patients with ulcerative colitis (UC) commencing vedolizumab to investigate fecal vedolizumab loss and its impact on serum levels and treatment outcomes. METHODS: FAVOUR recruited patients with moderate-to-severe UC commencing vedolizumab. Fecal vedolizumab levels (FVL) were measured at days 1, 4, and 7 and at weeks 2, 6, and 14. Trough serum vedolizumab levels (SVL) were measured at weeks 2, 6, and 14. RESULTS: In total, 36 patients were recruited, of whom 33 completed induction therapy. Fecal vedolizumab was detectable in 80/203 (39%) samples. Statistically significant, positive correlations were observed between FVL and clinical, biochemical, baseline endoscopic, and histologic disease activity at day 1, 4, and 7 as well as weeks 2 and 6. Week 14 clinical non-responders had higher FVL than responders at that timepoint (median 1.0 vs 0.0 µg/g, P = .004) but not at other timepoints. Area-under-the-curve analysis of FVL was used to quantify cumulative vedolizumab stool loss. This demonstrated significant differences between week 14 clinical responders and non-responders (44 µg/g/day, 95% CI: 0-128 vs 233 µg/g/day, 95% CI 0-1139, P < .0001), as well as between endoscopic responders and non-responders (48 µg/g/day, 95% CI 0-142 vs 179 µg/g/day, 95% CI 0-142, P = .0017), with non-responders having a higher rate of cumulative loss. CONCLUSIONS: Active UC results in fecal loss of vedolizumab. This correlates with lower SVL and decreased response to treatment. Fecal loss of vedolizumab may be a marker of disease activity and/or result in lower rates of drug exposure at a tissue level, negatively impacting response.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".