Comparing the accuracy of computed tomography enterography to balloon-assisted enteroscopy in the evaluation of small bowel Crohn’s disease
Bibliographic record
Abstract
Background: Evaluating small bowel Crohn's disease (SBCD) often relies on cross-sectional imaging (eg, computed tomography enterography [CTE]) and small bowel endoscopy (eg, balloon-assisted enteroscopy [BAE]). The accuracy of CTE for evaluating SBCD compared to BAE remains unclear and is assessed in this study. Methods: This single-centre retrospective study included patients with SBCD who underwent both CTE and BAE within 6 months. Findings of active inflammation, long-segment disease, skip-segments, and presence of both strictures and high-grade strictures (HGS) were extracted from CTE and BAE reports and analyzed using BAE as the reference standard. Results: Sixty-three CTE and BAE pairings were identified. CTE was sensitive for assessing active inflammation (80.0%) and all strictures (92.1%) and specific for long-segment inflammation (95.0%) and HGS (87.2%). Sensitivity was low for HGS (60.9%) and long-segment inflammation (50.0%), with poor specificity for all strictures (68.4%). In surgically naïve bowel, accuracy improved for active inflammation (sensitivity: 83.3%, specificity: 100%) and worsened for HGS (sensitivity: 42.9%, specificity: 84.2%). In postsurgical bowel, CTE sensitivity for HGS improved to 68.8%. Conclusion: Computed tomography enterography accurately detected active inflammation and fibrostenotic disease but may not be sufficient to rule out clinically significant findings such as HGS. The accuracy of CTE varied between surgically naïve and postsurgical bowel. CTE remains an important modality for evaluation of SBCD and should be used in combination with BAE when clinical discrepancy arises.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".