Diagnostic Value of Endoscopic and Endoscopic Ultrasound Characteristics of Duodenal Submucosal Tumour-Like Heterotopic Gastric Mucosa
Bibliographic record
Abstract
OBJECTIVE: Recent studies have reported that duodenal heterotopic gastric mucosa (HGM) has been observed in 8.9% of patients who undergo esophagogastroduodenoscopy. However, there are few reports concerning the endoscopic and endoscopic ultrasound characteristics of submucosal tumour-like HGM in the duodenum. METHODS: Endoscopic, endoscopic ultrasound (EUS) and histological findings were analyzed in six patients with submucosal tumour-like HGM, which were confirmed by pathological examination of biopsy or endoscopic polypectomy specimens. RESULTS: Endoscopically, the lesions appeared as a solitary, sessile submucosal tumour-like mass with a depression at the top. In four of six patients, small granular structures were found in the depressed area of the mass. On EUS, all masses demonstrated a heterogeneous pattern, among which four patients presented anechoic areas while two patients showed no anechoic areas. All lesions were localized within the mucosa and submucosa on EUS. Histologically, they consisted of gastric glands and some dilated glands, and were covered with normal duodenal epithelium. In four of six lesions, the tumours were composed of gastric-type foveolar epithelium showing papillary growth, fundic glands and pyloric glands, while the others consisted of gastric-type foveolar epithelium and pyloric glands. CONCLUSION: A heterogeneous pattern on EUS and small granular structures on esophagogastroduodenoscopy represent valuable diagnostic features of submucosal tumour-like HGM.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".