Bowel Ultrasonography in the Management of Crohnʼs Disease. A Review with Recommendations of an International Panel of Experts
Bibliographic record
Abstract
BACKGROUND: Bowel ultrasonography (US) is considered a useful technique for assessing mural inflammation and complications in Crohn's disease (CD). The aim of this review is to appraise the evidence on the accuracy of bowel US for CD. In addition, we aim to provide recommendations for its optimal use. METHODS: Publications were identified by literature search from 1992 to 2014 and selected based on predefined criteria: 15 or more patients; bowel US for diagnosing CD, complications, postoperative recurrence, activity; adequate reference standards; prospective study design; data reported to allow calculation of sensitivity, specificity, agreement, or correlation values; articles published in English. RESULTS: The search yielded 655 articles, of which 63 were found to be eligible and retrieved as full-text articles for analysis. Bowel US showed 79.7% sensitivity and 96.7% specificity for the diagnosis of suspected CD, and 89% sensitivity and 94.3% specificity for initial assessment in established patients with CD. Bowel US identified ileal CD with 92.7% sensitivity, 88.2% specificity, and colon CD with 81.8% sensitivity, 95.3% specificity, with lower accuracy for detecting proximal lesions. The oral contrast agent improves the sensitivity and specificity in determining CD lesions and in assessing sites and extent. CONCLUSIONS: Bowel US is a tool for evaluation of CD lesions in terms of complications, postoperative recurrence, and monitoring response to medical therapy; it reliably detects postoperative recurrence and complications, as well as offers the possibility of monitoring disease progression.
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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".