Defining Comprehensive Disease Control for Use as a Treatment Target for Ulcerative Colitis in Clinical Practice: International Delphi Consensus Recommendations
Bibliographic record
Abstract
BACKGROUND AND AIMS: Treatment of ulcerative colitis [UC] requires a patient-centric definition of comprehensive disease control that considers improvements in aspects not typically captured by classical landmark trial endpoints. In an international initiative, we reviewed aspects of UC that affect patients and/or indicate mucosal inflammation, to achieve consensus on which aspects to combine in a definition of comprehensive disease control, using a modified Delphi process. METHODS: The Delphi panel comprised 12 gastroenterologists and one patient advocate. Two gastroenterologists were elected as chairs and did not vote. To inform statements, we asked 18 patients and the panel members about their experiences of remission and reviewed published literature. Panel members voted on statements anonymously in three rounds, with a live discussion before Round 3. Consensus was met if ≥67% of the panel agreed. Statements without consensus in Rounds 1 and 2 were revised or discarded after Round 3. RESULTS: The panel agreed to measure individual patient benefit using a definition of comprehensive disease control that combines aspects currently measured in trials [rectal bleeding, stool frequency, disease-related quality of life, endoscopy, histological inflammatory activity, inflammatory biomarkers, and corticosteroid use] with additional patient-reported symptoms [bowel urgency, abdominal pain, extraintestinal manifestations, fatigue, and sleep disturbance]. The panel agreed on scoring systems and thresholds for many aspects. CONCLUSIONS: Using a robust methodology, we defined comprehensive disease control in UC. Next, we will combine the measurement and scoring of these aspects into a multicomponent tool and will adopt comprehensive disease control as a treatment target in clinical practice and trials.
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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 | 0.538 | 0.486 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.010 | 0.026 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".