Decision Support Tool Identifies Ulcerative Colitis Patients Most Likely to Achieve Remission With Vedolizumab vs Adalimumab
Bibliographic record
Abstract
BACKGROUND & AIMS: We have previously validated a clinical decision support tool (CDST) (vedolizumab CDST [VDZ-CDST]) for clinical and endoscopic remission with VDZ in ulcerative colitis (UC). We aim to expand the validation for predicting histoendoscopic mucosal improvement (HEMI) with VDZ vs adalimumab (ADA). METHODS: In a post hoc analysis of a clinical trial for VDZ vs ADA in moderate to severe UC (VARSITY trial; NCT02497469), comparative accuracy was evaluated for the VDZ-CDST among an external validation cohort of VDZ- and ADA-treated patients for week 52 HEMI (Mayo endoscopic subscore 0-1 and Geboes score <3.2). Comparative effectiveness of VDZ and ADA was assessed after stratifying the cohort by baseline probability of response to VDZ using the VDZ-CDST. RESULTS: A total of 419 patients were included. The majority of patients enrolled in the VARSITY trial had a high (61%) or intermediate (29%) baseline predicted probability of response to VDZ. The baseline VDZ-CDST score was significantly more likely to predict week 52 HEMI for VDZ (area under the curve , 0.712; 95% confidence interval, 0.636-0.787) relative to ADA-treated patients (area under the curve, 0.538; 95% confidence interval, 0.377-0.700; P < .001 for AUC comparison). A significant (P < .001) association was observed between the VDZ-CDST and measured VDZ drug exposure over 52 weeks. Superiority of VDZ to ADA was only observed in patients with a high baseline predicted probability of response to VDZ. CONCLUSIONS: Superiority of VDZ to ADA is dependent on baseline probability of response, and a VDZ-CDST is capable of identifying UC patients most appropriate for VDZ vs ADA.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".