Strategies to Distinguish Perianal Fistulas Related to Crohn’s Disease From Cryptoglandular Disease: Systematic Review With Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Management of perianal fistulas differs based on fistula type. We aimed to assess the ability of diagnostic strategies to differentiate between Crohn's disease (CD) and cryptoglandular disease (CGD) in patients with perianal fistulas. METHODS: We performed a diagnostic accuracy systematic review and meta-analysis. A systematic search of electronic databases was performed from inception through February 2021 for studies assessing a diagnostic test's ability to distinguish fistula types. We calculated weighted summary estimates with 95% confidence intervals for sensitivity and specificity by bivariate analysis, using fixed effects models when data were available from 2 or more studies. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess study quality. RESULTS: Twenty-one studies were identified and included clinical symptoms (2 studies; n=154), magnetic resonance imaging (MRI) characteristics (3 studies; n=296), ultrasound characteristics (7 studies; n=1003), video capsule endoscopy (2 studies; n=44), fecal calprotectin (1 study; n=56), and various biomarkers (8 studies; n=440). MRI and ultrasound characteristics had the most robust data. Rectal inflammation, multiple-branched fistula tracts, and abscesses on pelvic MRI and the Crohn's ultrasound fistula sign, fistula debris, and bifurcated fistulas on pelvic ultrasonography had high specificity (range, 80%-95% vs 89%-96%) but poor sensitivity (range, 17%-37% vs 31%-63%), respectively. Fourteen of 21 studies had risk of bias on at least 1 of the Quality Assessment of Diagnostic Accuracy Studies domains. CONCLUSIONS: Limited high-quality evidence suggest that imaging characteristics may help discriminate CD from CGD in patients with perianal fistulas. Larger, prospective studies are needed to confirm these findings and to evaluate if combining multiple diagnostic tests can improve diagnostic sensitivity.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".