Optimizing Patient Management in Crohn’s Disease in a Tertiary Referral Center: the Impact of Fast-Track MRI on Patient Management and Outcomes
Bibliographic record
Abstract
BACKGROUND AND AIMS: Rapid optimization of treatment algorithms and disease outcomes requires an objective measurement of disease activity in patients with Crohn's disease (CD). Our aim was to evaluate the impact of rapid-access to magnetic resonance imaging (MRI) on treatment optimization, clinical decision-making and outcomes for CD patients in a specialized tertiary care for inflammatory bowel disease (IBD) patients. METHODS: A cohort of 75 referral CD patients (median age: 34, IQR: 25-43 years) who had underwent 90 fast-track MR enterography (MRE) scans between January 2014 and June 2016 were retrospectively enrolled. The MRI results were compared to clinical activity scores and biomarkers (C-reactive protein). The immediate impact of fast-track MRI on clinical decision-making, including changes in medical therapy, the need of hospitalization and surgery were evaluated. RESULTS: The location of CD was ileo-colonic in 61% of the patients with perianal fistulas in 56% and previous surgeries in 55%. The indication for fast-track MRI scans was active disease (clinical or biomarker activity) in 55.6%. The radiological activity (including mild radiological signs to severe lesions) was detected in 94% of cases. Significant/severe MRI activity was depicted in 68% of these patients. Correlation between MRI radiological activity and clinical disease activity or colonoscopy was moderate (kappa: 0.609 and 0.652). A change in therapeutic strategy was made in 94.1% of cases with severe MRI radiological activity vs. 50% of patients without severe MRI radiological activity (p=0.001). Significant/severe MRI activity was followed by higher surgery rates among patients with clinical disease activity (50% vs. 12.5%; p=0.013). MRI performed on patients with clinical and biomarker remission identified disease activity in a significantly smaller proportion. CONCLUSIONS: Fast-track MRI had a great impact on patient management in CD patients with clinical or biomarker activity, leading to better patient stratification and faster optimization of the therapy (medical or surgical), while MRI revealed previously undiagnosed disease activity only in a small proportion of patients in clinical remission.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".