Outcomes predictors in endoscopic ultrasound-guided choledochoduodenostomy with lumen-apposing metal stent: Systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background and study aims EUS-guided choledochoduodenostomy (EUS-CDS) is a minimally invasive procedure used to treat malignant biliary obstruction (MBO) by transduodenal placement of a lumen-apposing metal stent (LAMS) into the extrahepatic bile duct. To identify factors that contribute to safe and effective EUS-CDS using LAMS, we performed a systematic review of the literature and meta-analysis. Methods The methodology of our analysis was based on PRISMA recommendations. Electronic databases (Medline, Scopus, EMBASE) were searched up to November 2022. Full articles that included patients with distal malignant biliary obstruction who underwent EUS-CDS using LAMS after failed endoscopic retrograde cholangiopancreatography were eligible. Random-effect meta-analysis was performed reporting pooled rates of technical success, clinical success, and adverse events (AEs) by means of a random model. Multivariate meta-regression and subgroup analysis were performed to assess possible associations between the outcomes and selected variables to assess the correlation between outcomes and different variables. Results were also stratified according to stent size. Results Twelve studies with 845 patients were included in the meta-analysis. Pooled technical and clinical success rates were 96% (95% confidence interval [CI] 94%-98%; I2 = 52.29%) and 96% (95%CI 95%-98%), respectively, with no significant association with baseline characteristics, such are sex, age, common bile duct diameter, or stent size. The pooled AE rate was 12% (95%CI: 8%-16%; I2 = 71.62%). The AE rate was significantly lower when using an 8 × 8 mm stent as compared with a 6 × 8 mm LAMS (odds ratio 0.59, 0.35–0.99; P = 0.04), with no evidence of heterogeneity (I2 = 0%). Conclusions EUS-CDS with LAMS is a safe and effective option for relief of MBO. Selecting an appropriate stent size is crucial for achieving optimal safety 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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".