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 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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".