Diagnostic Accuracy of Noninvasive Biomarkers and Imaging for Evaluating Postoperative Recurrence in Crohn’s Disease
Bibliographic record
Abstract
BACKGROUND & AIMS: Colonoscopy is recommended to monitor for Crohn's disease (CD) recurrence after surgical resection. However, repeated colonoscopy is invasive and resource-intensive. We conducted a systematic review and meta-analysis to evaluate the pooled diagnostic accuracy of noninvasive biomarkers and imaging measures for detecting endoscopic CD recurrence, as compared with colonoscopy. METHODS: A systematic review was conducted to January 31, 2024, to identify studies evaluating the diagnostic accuracy of C-reactive protein (CRP), fecal calprotectin, computed tomography and magnetic resonance enterography, or intestinal ultrasound (IUS) compared with colonoscopy for detecting CD recurrence. Estimates of sensitivity, specificity, and positive and negative likelihood ratios were pooled using a random-effects hierarchical summary receiver operating characteristic model. RESULTS: A total of 17 studies (N = 1080) evaluated inflammatory biomarkers and 20 studies (N = 1053) assessed imaging measures. The pooled sensitivity and specificity of CRP (threshold, 5.0 mg/L) were 0.45 (95% confidence interval [CI], 0.33-0.58) and 0.83 (95% CI, 0.68-0.92), respectively. Fecal calprotectin (threshold, 50 μg/g) was moderately sensitive 0.76 (95% CI, 0.70-0.82) but less specific 0.66 (95% CI, 0.56-0.75). Sensitivity for computed tomography enterography/magnetic resonance enterography and IUS was 0.89 (95% CI, 0.73-0.96) and 0.92 (95% CI, 0.75-0.96); specificity was 0.65 (95% CI, 0.43-0.82) and 0.76 (95% CI, 0.52-0.90), respectively. Using optimized radiographic parameters for IUS, specificity was improved to 0.85 (95% CI, 0.71-0.93). CONCLUSIONS: The high sensitivity of fecal calprotectin (<50 μg/g) and cross-sectional imaging can help reduce the need for invasive and costly colonoscopy monitoring for CD recurrence after surgery. Applying optimal definitions of sonographic recurrence by IUS parameters may further improve specificity for making therapeutic decisions without endoscopy.
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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 | 0.025 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".