A121 ULTRASOUND VERSUS ENDOSCOPY, SURGERY OR PATHOLOGY FOR THE DIAGNOSIS OF SMALL BOWEL CROHN’S DISEASE AND IT’S COMPLICATIONS
Bibliographic record
Abstract
The diagnosis of Small Bowel Crohn’s Disease (SBCD) is challenging given poor endoscopic accessibility. Ultrasound (US) represents an accessible, cost-effective, minimally invasive, radiation-free diagnostic option. Primary objective: To determine the diagnostic accuracy of US in SBCD compared to endoscopic visualization (enteroscopy, VCE or ileocolonoscopy), surgery and/or pathology. Secondary objective: To determine the accuracy of US in determining the presence of SBCD-related complications (fistula, abscess, stricture). MEDLINE, EMBASE and CENTRAL were searched for prospective cohort studies. Full-text review and data extraction were performed by a single reviewer. Studies were assessed for their methodological quality using the QUADAS criteria. 1082 unique references were identified. 20 studies were finally included. All studies were at low-moderate risk of bias. Trans-abdominal US (TAUS) yielded moderately high sensitivity and specificity for the diagnosis of SBCD and its post-operative recurrence. Detection was more accurate for severe post-operative recurrence. The diagnostic accuracy of US in stricture and abscess detection was high. Contrast enhancement improved the detection of abscess. The diagnostic detection of fistulas had moderate accuracy. Entero-enteric and entero-mesenteric fistulas were most accurately identified. US is an accurate radiological modality to diagnose SBCD in those with known or suspected disease. It can be used with success to diagnose post-operative recurrence and can be used accurately to identify complications, especially with the aid of contrast enhancement. None
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".