Accuracy of point‐of‐care intestinal ultrasound for Crohn's disease
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasound (POCUS), performed by a gastroenterologist, provides safe and convenient imaging allowing for immediate clinical decision in Crohn's disease. The minimum training required to gain competency, its accuracy and clinical utility requires evaluation. METHODS: In this pilot study, Crohn's disease activity and extent were assessed using POCUS (performed by a single gastroenterologist following the completion of 200 supervised scans), magnetic resonance enterography (MRE) and ileo-colonoscopy. The presence of complications was assessed by POCUS and MRE. Accuracy of POCUS was analysed with respect to MRE and ileo-colonoscopy. Agreement between modalities was assessed using kappa coefficient. RESULTS: Forty-two patients had a POCUS paired with MRE. Thirty-eight patients had a POCUS paired with ileo-colonoscopy. When compared to MRE, POCUS was accurate in the assessment of disease activity (sensitivity 87.5%, specificity 61.1%, ROC 0.74), extent (sensitivity 77.8%, specificity 83.3%, ROC 0.81) and complications (sensitivity 85.7%, specificity 94.3%, ROC 0.90). Agreement between POCUS and MRE was moderate (kappa estimates 0.50, P < 0.001, 0.61, P < 0.001 and 0.76, P < 0.001) for disease activity, extent and complications, respectively. When compared to ileo-colonoscopy, POCUS was accurate in the assessment of disease activity (sensitivity 72%, specificity 86%, ROC 0.79) and extent (sensitivity 85.7%, specificity 86%, ROC 0.86). For POCUS and ileo-colonoscopy, kappa estimates were 0.55, P < 0.001 for disease activity and 0.62, P < 0.001 for disease extent. CONCLUSION: POCUS performed by a gastroenterologist after completion of limited training is accurate for assessing Crohn's disease activity, extent and the presence of complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".