The Korean Society of Gastroenterology & SIDDS 2064 : Slide Session ; K-UG-16 : Upper GI Tract ; Endoscopic Submucosal Dissection of Gastric Subepithelial Tumors: A Systematic Review and Meta- Analysis
Bibliographic record
Abstract
Background: The treatment of gastric subepithelial tumors (SETs) depends on surgical resection. The aim of this study was to evaluate the current evidence about therapeutic outcomes of endoscopic submucosal dissection (ESD) technique for the treatment of gastric SET. Methods: A systematic literature review was conducted using the core databases. Data about complete resection rate and procedure-related perforation rate were extracted and analyzed. A random effect model was applied. The methodological quality of the enrolled studies was assessed by the Newcastle-Ottawa Scale. Sensitivity analyses were performed by the origin of the gastric wall layer of SETs. Publication bias was evaluated through the funnel plot, trim and fi ll method, Egger`s test, and rank correlation test. Results: A total of 290 SETs in 288 patients was enrolled from 9 studies (44 SETs originated from the submucosal layer, 246 SETs originated from the muscularis propria layer). The mean diameter of the lesions were ranged from 17.99 to 38mm (mean value). Overall, pooled complete resection rate was estimated as 86.2% (95%CI: 78.9%-91.3%, P< 0.001). If limited to the lesions originated from the submucosal layer, the pooled complete resection rate was 91.4% (95%CI: 77.9%-97%, P< 0.001). If limited to the lesions originated from the muscularis propria layer, the pooled complete resection rate was 84.4% (95%CI: 78.7%-88.8%, P< 0.001). Pooled procedure-related gastric perforation rate was 13% (95%CI: 9.4%-17.6%, P< 0.001). Among the 34 incomplete resected SETs, 20 cases were leiomyoma and 10 cases were GIST. Sensitivity analyses showed consistent results. Publication bias was not detected. Conclusions: In this analysis, ESD including endoscopic muscularis dissection is technically feasible procedure for the treatment of SETs. However, selection bias is suspected from the enrolled studies. For the development of proper indication about ESD for SETs, further studies are needed.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".