P413 A simple scoring tool predicts exposure–response relationship, onset of action, response to interval shortening, and surgical risk with vedolizumab therapy for Crohn’s disease
Bibliographic record
Abstract
We previously created and validated a clinical decision support tool (CDST) for predicting response to vedolizumab (VDZ) in Crohn’s disease (CD). We now aim to further validate this tool in an additional CD cohort and assess its performance for predicting other health outcomes. Using GEMINI II data, we explored correlations between VDZ exposure and onset of action across CDST-predicted probability of response groups (low, intermediate, high). The operating properties of the CDST for prediction of clinical remission and onset of action in the GETAID VDZ cohort were evaluated. In the GETAID and VICTORY cohorts, response to dose optimisation was assessed, and in the VICTORY cohort, we assessed the ability of the CDST to predict risk of surgery while on active therapy. A linear relationship was observed between CDST-predicted probability of response groups, VDZ exposure, onset of action, and efficacy in the GEMINI cohort for Week 2 through Week 52 (p < 0.001). In the GETAID cohort, the CDST predicted clinical remission at Week 14 (AUC 0.68), and a significant difference in speed of onset of action was observed between low- and intermediate–high-probability groups (p = 0.04). In both the GETAID and VICTORY cohorts, only patients in the low-probability group significantly benefited from shortening of VDZ intervals to Q4 weeks for non-response. In the GETAID cohort, a single infusion at Week 10 for patients in the low-probability group overcame differences in speed of onset of action seen between this group and the intermediate–high-probability group. In the VICTORY cohort, the CDST predicted a 2-fold increase in risk for surgery over 12 months of VDZ therapy among low–intermediate-probability patients compared with high-probability patients (HR 2.06, 95% CI 1.33–3.21). The CD VDZ CDST demonstrated good performance during external validation in the GETAID cohort. This tool was able to prognosticate VDZ exposure-efficacy relationships and speed of onset of action, identify patients who would most benefit from interval shortening for lack of response, and stratify patients at greatest risk for surgery while on active therapy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".