A clinical decision support tool may help to optimise vedolizumab therapy in Crohn’s disease
Bibliographic record
Abstract
BACKGROUND: A clinical decision support tool (CDST) has been validated for predicting treatment effectiveness of vedolizumab (VDZ) in Crohn's disease. AIM: To assess the utility of this CDST for predicting exposure-efficacy and disease outcomes. METHODS: Using data from three independent datasets (GEMINI, GETAID and VICTORY), we assessed clinical remission rates and measured VDZ exposure, rapidity of onset of action, response to dose optimisation and progression to surgery by CDST-defined response groups (low, intermediate and high). RESULTS: A linear relationship existed between CDST-defined groups, measured VDZ exposure, rapidity of onset of action and efficacy in GEMINI through week 52 (P < 0.001 at all time points across three CDST-defined groups). In GETAID, CDST predicted differences in clinical remission at week 14 (AUC = 0.68) and rapidity of onset of action (P = 0.04) between probability groups. The high-probability patients did not benefit from shortening of infusion intervals, and differences in onset of action between the high-intermediate and low-probability groups within GETAID were no longer significant when including low-probability patients who received a week 10 infusion. CDST predicted a twofold increase in surgery risk over 12 months of VDZ therapy among low- to intermediate-probability vs high-probability patients (adjusted HR 2.06, 95% CI 1.33-3.21). CONCLUSIONS: We further extended the clinical utility of a previously validated VDZ CDST, which accurately predicts at baseline exposure-efficacy relationships and rapidity of onset of action and could be used to help identify patients who would most benefit from interval shortening and those most likely to require 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 | 0.014 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".