Small Bowel Capsule Endoscopy in the Management of Established Crohnʼs Disease
Bibliographic record
Abstract
BACKGROUND: Multiple studies have established the superior diagnostic accuracy of video capsule endoscopy (VCE) for the diagnosis of small bowel (SB) Crohn's disease (CD). However, data on the clinical impact of VCE in patients with established CD are scarce. The aim of this study was to examine the impact and safety of VCE on the clinical management of patients with established CD. METHODS: A retrospective, multicenter, cross-sectional study. The study cohort included consecutive patients with established SB CD who underwent VCE in 4 tertiary referral centers (1 Canada, 1 Sweden, and 2 United Kingdom) from January 2008 to October 2013. Patients were excluded if VCE was performed as a part of the initial diagnostic workup. The presence of SB mucosal inflammation was quantified using the Lewis score. Inflammatory biomarkers (C-reactive protein and fecal calprotectin) were measured and correlated with the Lewis score. RESULTS: The study included 187 patients. No SB inflammation was observed in 28.4%, mild-to-moderate inflammation in 26.6%, and moderate-to-severe inflammation in 45% of the patients (median Lewis score, 662; range, 0-6400). A change in management was recommended in 52.3% of the patients based on VCE findings. Elevated C-reactive protein, fecal calprotectin, or the combination of both were poorly correlated with significant SB inflammation. SB capsule retention occurred in 4 patients (2.1%). CONCLUSIONS: VCE in patients with established CD is safe, and the results often have a significant clinical impact. VCE should not be limited to CD patients with positive inflammatory markers because their predictive value for significant SB inflammation is poor.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".