A233 ENDOSCOPIC ASSISTED ENUCLEATION OF SMALL GASTRIC SUBEPITHELIAL TUMORS: AN EARLY SINGLE CENTER EXPERIENCE
Bibliographic record
Abstract
Gastric subepithelial tumors (SETs) are not uncommonly discovered on routine endoscopy. They are often associated with a diagnostic challenge and require follow up to ensure no interval growth suggestive of malignant potential. Endoscopic assisted enucleation is a technique to facilitate a tissue diagnosis and removal. To report an early single center experience with endoscopic assisted enucleation of small gastric SETs. A retrospective case series of prospectively collected data for patients who underwent endoscopic assisted enucleation of small gastric SETs at Vancouver General Hospital. All patient underwent prior endoscopic ultrasound (EUS) for lesion characterization. Endoscopic interventions include: suction-ligation-unroofing-biopsy (SLUB) or retraction-ligation-unroofing-biopsy (RLUB). The technical success of the procedures, diagnostic yield and complication were described. A total of 7 patients underwent endoscopic assisted enucleation between October 2015 and October 2016. The mean age was 59 years and 71% were female. Gastric SETs were incidentally discovered on gastroscopy in 5 patients and on CT scan in 1 patient. 1 patient presented with anemia with ulcerated SET. On EUS, the median size was 2 cm (range 1.1 to 3), 3 arise from the fourth layer, 2 from the third and 2 from the second layer. 2 patients underwent EUS guided biopsies and both were non diagnostic. The initial endoscopic procedure was successful in 6 patients but failed in 1 patient. This patient had a 3 cm SET and is awaiting surgery. Biopsies after unroofing provided specific diagnosis in 4 (gastrointestinal stromal tumor, neuroma, lipoma, heterotrophic pancreas) and non-specific diagnosis in 2. There were no complications. Follow up endoscopy data was available in 4 patients: 3 had no visible lesion, 1 had the endoloop remains in place. Endoscopic assisted enucleation of gastric subepithelial tumors is technically feasible and safe. It facilitates diagnosis and potential cure limiting the need for surveillance. 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
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".