ECCO Topical Review on Predictive Models on Inflammatory Bowel Disease Disease Course and Treatment Response
Bibliographic record
Abstract
BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) poses a clinical challenge due to its variable progression and treatment response. Despite the development of predictive models, their clinical application remains limited due to validation and methodological inconsistencies. The current topical review examines existing predictive models, assesses their relevance, and discusses the barriers to their clinical implementation. METHODS: An expert panel formed by European Crohn's and Colitis Organisation, including gastroenterologists, surgeons, and clinical epidemiologists, reviewed predictive models on IBD disease course and treatment response. Delphi methodology was applied to develop practice position statements. A practice position was set when at least 80% of participants reached agreement on a recommendation. RESULTS: Fourteen practice positions and 2 perspective points were developed, highlighting factors included in models predicting IBD disease course and treatment response identified in the literature and barriers to clinical implementation. The appropriate methodological approaches for model development and validation have been defined, while methodological barriers to tackle have been identified. Perspectives on the inclusion of relevant biomarkers, and flexible study design have been outlined. CONCLUSIONS: This topical review offers practice recommendations and guidance for future predictive models on IBD disease course and treatment response including their implementation in clinical practice.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".