Composite Assessment Using Intestinal Ultrasound and Calprotectin Is Accurate in Predicting Histological Activity in Ulcerative Colitis: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Beyond endoscopic remission, histological remission in ulcerative colitis (UC) is predictive of clinical outcomes. Intestinal ultrasound (IUS) may offer a noninvasive surrogate marker for histological activity; however, there are limited data correlating validated ultrasound and histological indices. AIM: Our aim was to determine the correlation of IUS activity in UC with a validated histological activity index. METHODS: Twenty-nine prospective, paired, same-day IUS/endoscopy/histology/fecal calprotectin (FC) cases were included. Intestinal ultrasound activity was determined using the Milan Ultrasound Criteria, histological activity using the Nancy Histological Index, endoscopic activity using Mayo endoscopic subscore and Ulcerative Colitis Endoscopic Index of Severity, and clinical activity using the Simple Clinical Colitis Activity Score. RESULTS: Histological activity demonstrated a significant linear association with overall IUS activity (coefficient 0.14; 95% CI, 0.03-0.25; P = .011). Intestinal ultrasound activity was also significantly associated with endoscopic activity (0.32; 95% CI, 0.14-0.49; P < 0.001), total Mayo score (0.31; 95% CI, 0.02-0.60; P = .036) but not FC (0.10; 95% CI, -0.01 to 0.21; P = .064) or clinical disease activity (0.04; 95% CI, -0.21 to 0.28; P = .768). A composite of IUS and FC showed the greatest association (1.31; 95% CI, 0.43-2.18; P = .003) and accurately predicted histological activity in 88% of cases (P = .007), with sensitivity of 88%, specificity 80%, positive predictive value 95%, and negative predictive value 57%. CONCLUSIONS: Intestinal ultrasound is an accurate noninvasive marker of histological disease activity in UC, the accuracy of which is further enhanced when used in composite with FC. This can reduce the need for colonoscopy in routine care by supporting accurate point-of-care decision-making in patients with UC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".