Transcutaneous Bowel Sonography for Inflammatory Bowel Disease Is Sensitive and Specific When Performed in a Nonexpert Low‐Volume North American Center
Bibliographic record
Abstract
OBJECTIVES: Transcutaneous bowel sonography is a nonionizing imaging modality used in inflammatory bowel disease. Although available in Europe, its uptake in North America has been limited. Since the accuracy of bowel sonography is highly operator dependent, low-volume centers in North America may not achieve the same diagnostic accuracy reported in the European literature. Our objective was to determine the diagnostic accuracy of bowel sonography in a nonexpert low-volume center. METHODS: All cases of bowel sonography at a single tertiary care center during an 18-month period were reviewed. Bowel sonography was compared with reference standards, including small-bowel follow-through, computed tomography, magnetic resonance imaging, colonoscopy, and surgical findings. RESULTS: A total of 103 cases were included for analysis during the study period. The final diagnoses included Crohn disease (72), ulcerative colitis (8), hemolytic uremic syndrome (1), and normal (22). The sensitivity and specificity of bowel sonography for intestinal wall inflammation were 87.8% and 92.6%, respectively. In the subset of patients who had complications of Crohn disease, the sensitivity and specificity were 50% and 100% for fistulas and 14% and 100% for strictures. One patient had an abscess, which was detected by bowel sonography. Abnormal bowel sonographic findings contributed to the escalation of treatment in 55% of cases. CONCLUSIONS: Bowel sonography for inflammatory bowel disease can be performed in low-volume centers and provides diagnostic accuracy for luminal disease comparable with published data, although it is less sensitive for complications of Crohn disease.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".