EUS-guided gastroenterostomy in management of benign gastric outlet obstruction
Bibliographic record
Abstract
Background and study aims: Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) in malignant gastric outlet obstruction (GOO) appears to be promising; however, its role in benign GOO is unclear. The aim of this study was to ascertain the clinical efficacy and safety of EUS-GE in benign GOO. Patients and methods: This was an international retrospective series involving 5 tertiary centers. Consecutive patients who underwent EUS-GE between 1/2013 - 10/2016 for benign GOO were included. The primary endpoint was the rate of clinical success defined as ability to tolerate oral intake without vomiting. Secondary endpoints included technical success and rate of adverse events (AE). Results: Overall, 26 patients (46.2 % female; mean age 57.7 ± 13.9 years) underwent EUS-GE for benign GOO due to strictures from chronic pancreatitis (n = 11), surgical anastomosis (n = 6), peptic ulcer disease (n = 5), acute pancreatitis (n = 1), superior mesentery artery syndrome (n = 1), caustic injury (n = 1), and hematoma (n = 1). Technical success was achieved in 96.2 %. Dilation of the lumen apposing metal stent was performed in 13/25 (52 %) with a mean maximum diameter of 14.6 ± 1.0 mm. Mean procedure time was 44.6 ± 26.1 min. Clinical success was observed in 84.0 % with a mean time to oral intake of 1.4 ± 1.9 days and a median follow-up of 176.5 (IQR: 47 - 445.75) days. Rate of unplanned re-intervention was 4.8 %. 3 AE were noted including 2 misdeployed stents and 1 gastric leak needing surgical intervention following elective GE stent removal. Conclusions: EUS-GE is a promising treatment for benign GOO. Larger and prospective data are needed to further validate this novel endoscopic technique in treating benign GOO of various etiologies.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".