Standardizing Success and Troubleshooting in EUS-Guided Gastroenterostomy
Bibliographic record
Abstract
EUS-guided gastroenterostomy (EUS-GE) is rapidly emerging as a pivotal procedure in the management of gastric outlet obstruction due to its advantages over historical comparators such as surgical gastroenterostomy and endoscopic placement of gastroduodenal stents. The ability to create a stable surgical-range connection between 2 lumens, distant from the tumor, with the minimally invasive nature of an endoscopic procedure, offers high clinical efficacy, acceptable safety, and low recurrence rates. However, widespread adoption is impeded by the steep learning curve and lack of standardized methodologies. Like other interventional EUS procedures, EUS-GE utilizes lumen apposing metal stents. Unlike drainage procedures, the target in EUS-GE is a mobile structure with a virtual resting caliber that needs to be distended to create the connection, making misdeployment a significant drawback. This comprehensive illustrated technical review dissects the general and specific technical principles of EUS-GE covering the equipment, scene, settings, and endoscopic signs of correct and incorrect placement. It provides a deeper insight into the wireless simplified EUS-GE technique, the EUS-guided double-balloon-occluded gastrojejunostomy bypass, and the direct technique. Through pragmatic tips, expert advice, and elucidative step-by-step videos, a systematic roadmap for mastering this intricate procedure is presented. By addressing common challenges and providing troubleshooting strategies, this review aims to demystify EUS-GE, equipping practitioners with the tools to achieve reproducible and optimal outcomes.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".