EUS-guided gastroenterostomy vs. surgical gastrojejunostomy and enteral stenting for malignant gastric outlet obstruction: a meta-analysis
Bibliographic record
Abstract
Abstract Background and study aims Malignant gastric outlet obstruction (MGOO) is traditionally treated with surgical gastrojejunostomy (SGJ), which is effective but associated with high rates of morbidity, or endoscopic stenting (ES), which is less invasive but associated with significant risk of stent dysfunction and need for reintervention. Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) provides a robust bypass without the invasiveness of surgery. Methods We performed a systematic review and meta-analysis comparing EUS-GE to SGJ and ES for MGOO. Electronic databases were searched from inception through February 2022. A meta-analysis was performed with results reported as odds ratios (ORs) with 95% confidence intervals (CIs) using random effects models. Primary outcomes included clinical success without recurrent GOO and adverse events (AEs). Results Sixteen studies involving 1541 patients were included. EUS-GE was associated with higher clinical success without recurrent GOO compared to ES or SGJ [OR 2.60, 95% CI1.58–4.28] and compared to ES alone [OR 5.08, 95% CI 3.42–7.55], but yielded no significant difference compared to SGJ alone [OR 1.94, 95% CI 0.97–3.88]. AE rates were significantly lower for EUS-GE compared to ES or SGJ grouped together [OR 0.34, 95% CI 0.20–0.58], or SGJ alone [OR 0.17, 95% CI 0.10–0.30] but were not significant different versus ES alone [OR 0.57, 95% CI 0.29–1.14]. Conclusions EUS-GE is the most successful approach to treating MGOO, exhibiting a lower risk of recurrent obstruction compared to ES, and fewer AEs compared to SGJ.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".