A153 STRATEGIES TO DISTINGUISH PERIANAL FISTULAS RELATED TO CROHN’S DISEASE FROM CRYPTOGLANDULAR DISEASE: SYSTEMATIC REVIEW WITH META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Differentiating between perianal fistulas related to cryptoglandular disease (CGD) and Crohn’s disease (CD) is essential to guide disease specific management. Aims We aimed to assess the ability of diagnostic strategies to differentiate between CD from CGD in patients with perianal fistulas. Methods We performed a diagnostic accuracy systematic review and meta-analysis. Electronic databases (MEDLINE, Embase, Web of Science, CENTRAL) were systematically searched from inception through February 2021 for studies that assessed 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 was available from two or more studies. The QUADAS tool was used to assess study quality. Results Twenty-one studies were identified and included: clinical symptoms (2 studies; n = 154 patients), MRI characteristics (3 studies; n = 296 patients), ultrasound characteristics (7 studies; n = 1003 patients), video capsule endoscopy (2 studies; n = 44 patients), fecal calprotectin (1 study; n = 56 patients) and various biomarkers (8 studies; n = 440 patients). 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 twenty-one studies had risk of bias on at least one of the QUADAS 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. Funding Agencies None
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.050 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".