Bedside Intestinal Ultrasound Performed in an Inflammatory Bowel Disease Urgent Assessment Clinic Improves Clinical Decision-Making and Resource Utilization
Bibliographic record
Abstract
Abstract Background Patients with inflammatory bowel disease (IBD) require accessible, timely, and noninvasive strategies to monitor disease. The aim was to assess the integration of intestinal ultrasound (IUS) on decision-making and endoscopy utilization in a standardized care pathway. Methods This prospective, multicenter, international, observational cohort study included patients seen within a centralized model for IBD care was conducted during the COVID pandemic. Patients were evaluated with IUS alone or in combination with an in-clinic, unsedated sigmoidoscopy. Demographic, clinical, laboratory, and imaging data, clinical decisions, and need for urgent endoscopy, hospitalization, and surgeries were recorded. Results Of the 158 patients included, the majority had an established diagnosis of Crohn’s disease (n = 123, 78%), and 47% (n = 75) of patients were on biologic therapy. IUS identified active inflammation in 65% (n = 102) of patients, and strictures in 14% (n = 22). Fecal calprotectin levels correlated with inflammation detected on IUS (median of 50 μg/g [Q1–Q3: 26–107 μg/g] without inflammation and 270 μg/g [Q1–Q3: 61–556 μg/g] with inflammation; p = 0.0271). In the majority of patients, clinical assessment with IUS led to an acute change in IBD-specific medications (57%, n = 90) and avoided or delayed the need for urgent endoscopy (85%, n = 134). Four patients were referred for urgent surgical consultation. Conclusions Point-of-care IUS used in a flare clinic pathway is a useful strategy to improve effective IBD care delivery and to assist in therapeutic management decisions, in many cases avoiding the acute need for endoscopy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".