Defining Magnetic Resonance Imaging Treatment Response and Remission in Crohn’s Disease: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging is increasingly used to assess treatment response in Crohn's disease clinical trials. We aimed to describe the definition of MRI response and remission as assessed by magnetic resonance enterography [MRE] to evaluate treatment efficacy in these patients. METHODS: Electronic databases were searched up to May 1, 2023. All published studies enrolling patients with inflammatory bowel disease and assessment of treatment efficacy with MRE were eligible for inclusion. RESULTS: Eighteen studies were included. All studies were performed in patients with Crohn's disease. The study period ranged from 2008 to 2023. The majority of studies used endoscopy as the reference standard [61.1%]. MRE response was defined in 11 studies [61.1%]. Five scores and nine different definitions were proposed for MRE response. MRE remission was defined in 12 studies [66.7%]. Three scores and nine different definitions for MRE remission were described. The MaRIA score was the most frequent index used to evaluate MRE response [63.6%] and remission [41.7%]. MRE response was defined as MaRIA score <11 in 63.6% of studies using this index. In 60% of studies using the MaRIA score, MRE remission was defined as MaRIA score <7. In addition, 11 different time points of assessment were reported, ranging from 6 weeks to years. CONCLUSION: In this systematic review, significant heterogeneity in the definition of MRE response and remission evaluated in patients with Crohn's disease was observed. Harmonization of eligibility and outcome criteria for MRE in Crohn's Disease clinical trials is needed.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".