Systematic review with meta‐analysis: magnetic resonance enterography signs for the detection of inflammation and intestinal damage in Crohn's disease
Bibliographic record
Abstract
BACKGROUND: In the treatment of Crohn's disease (CD), mucosal healing has become a major goal, with the hope of avoiding intestinal damage from chronic inflammation. Magnetic resonance enterography (MRE) has emerged as a non-invasive means of monitoring inflammation and damage. AIMS: As part of the development of MRE-based multi-item measures of inflammation and damage for paediatric studies, we carried out a systematic review and meta-analysis to identify MRE variables used to describe these two distinct concepts. METHODS: 2501 studies of MRI and CD were identified. Studies written in any language reporting individual MRE signs for patients diagnosed with CD were included. Two-hundred-and-forty-four studies were fully reviewed and 62 were included (inflammation, n = 51; damage, n = 24). Sensitivity, specificity and associated confidence intervals were calculated, and hierarchical summary ROC curves were constructed for each MRE sign. RESULTS: A total of 22 MRE signs were used to reflect inflammation, and 9 to reflect damage. Diagnostic accuracy of MRE signs of inflammation and damage was heterogeneous; however, wall enhancement, mucosal lesions and wall T2 hyperintensity were the most consistently useful for inflammation (most sensitivities >80% and specificities >90%), and detection of abscess and fistula were most consistently useful for damage (most sensitivities >90%, specificities >95%). CONCLUSIONS: Identifying the best MRE variables to reflect inflammation and damage will maximise the utility of this rapidly emerging technique and is the first stage of constructing MRE-based indices for evaluating inflammation and intestinal damage.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".