Clinic-based Point of Care Transabdominal Ultrasound for Monitoring Crohn’s Disease: Impact on Clinical Decision Making
Bibliographic record
Abstract
BACKGROUND AND AIMS: The use of cross-sectional imaging is important to characterise inflammatory bowel disease [IBD] activity, extent, and location and to exclude complications, regardless of symptoms. The aim of this study was to evaluate the impact of routine use of sonography in the management of inflammatory bowel disease. METHODS: A total of 49 patients with Crohn's disease were prospectively evaluated. Clinical symptoms (Harvey-Bradshaw Index [HBI]), disease character, serological markers of inflammation [C-reactive protein], and endoscopic evaluation were collected and reviewed by two independent IBD-specialty physicians. Clinical decisions regarding management were recorded. A separate, blinded physician then performed bowel ultrasound [US] and graded disease activity:] as inactive, mild, or active. A second blinded physician read and graded a sub-set of the US images. Clinical decisions of both IBD-physicians after US were independently recorded. Changes in clinical management following US information and inter-rater agreement on US disease activity parameters were evaluated. The concordance between US, CRP and clinical symptoms [HBI] were analysed. Follow-up data after US evaluation were collected. RESULTS: Clinical decisions were changed after ultrasound assessment in 30/49 [60%] and 28/48 [58%] of cases, for each physician respectively [p < 0.0001 for each]. Many [59%] of the patients seen in clinic were asymptomatic with an HBI of 3 or less [n = 29]; however, 52% [n = 15] of these had active disease found on US, resulting in alterations in clinical management. The agreement in overall score between the US reviewers was good, ĸ = 0.749 [0.5814, 0.9180], p < 0.001. CONCLUSIONS: Clinic-based point of care US can play a significant role in guiding therapeutic management and is an important adjunct to routine clinical and laboratory assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".