High definition i-SCAN endoscopy with water immersion technique accurately reflects histological severity of celiac disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Severe villous atrophy can be revealed with conventional white light endoscopy (WLE), however, milder grades or patchy villous atrophy are more difficult to detect. Novel endoscopic techniques such as high definition i-SCAN endoscopy with the water immersion technique (i-SCAN-HDWI) may provide the ability to visualize duodenal villi more accurately. We aimed to determine the performance of i-SCAN-HDWI in evaluating the severity of histological damage in the duodenum of patients with celiac disease. PATIENTS AND METHODS: A retrospective cohort study was performed in a single tertiary academic endoscopic center. We studied 58 patients (46 women; median age 36.5 years, range 18 - 72 years) with positive anti-TTG IgA antibody. The villous pattern of the second part of the duodenum was assessed by WLE and i-SCAN-HDWI. The endoscopic grades in both techniques were correlated using Marsh histologic grades by Spearman correlation coefficient. The diagnostic accuracy of i-SCAN-HDWI for detection of patchy or complete atrophy of the villi was evaluated. RESULTS: A significant correlation was demonstrated between endoscopic grade using i-SCAN-HDWI and Marsh histologic grade (r = 0.732; P < 0.00001). The correlation between WLE grade and Marsh histologic grade was inferior to i-SCAN-HDWI (r = 0.31; P = 0.01). The sensitivity of i-SCAN-HDWI was 96 % (95 %CI: 85 - 99 %) and the specificity was 63 % (95 %CI: 26 - 90 %) in diagnosing abnormal biopsy consistent with celiac disease. CONCLUSION: i-SCAN-HDWI endoscopy can reflect the histological severity of celiac disease more accurately than conventional WLE alone. This novel endoscopic imaging can improve the diagnostic yield of duodenal biopsies in celiac patients, especially for those with a patchy distribution of villous damage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".