EUS-guided gallbladder drainage as a rescue in distal malignant biliary obstruction: A systematic review with meta-analysis
Bibliographic record
Abstract
Abstract Patients with distal malignant biliary obstruction (dMBO) needing biliary drainage (BD) undergo ERCP as a first approach. EUS–guided gallbladder drainage (EUS-GBD) is now accepted as a rescue alternative for the palliation of jaundice in those patients with dMBO who fail ERCP and cannot undergo EUS-BD. This is a systematic review with meta-analysis for evaluating the efficacy and safety of EUS-GBD in this scenario. A comprehensive search through the main database platforms was conducted to May 2024. Pooled estimates were obtained using a fixed-effects model with the generic inverse variance method. Study quality was evaluated using the Newcastle-Ottawa quality assessment scale (NOS). Heterogeneity was evaluated with I 2 statistic. Clinical success, adverse events (AEs) rate, and reintervention rate were the main outcomes. Sensitivity analyses were also conducted. Eight studies including 183 patients were identified. Pooled clinical success was 89% (95% CI, 84%–93%). The pooled clinical success of full-text publication was 88% (95% CI, 83%–93%; I 2 = 0%). Reintervention rate was 8% (95% CI, 4%–12%; I 2 = 0%). The overall AE rate was 10% (95% CI, 6%–15%; I 2 = 0%). The NOS allocated moderate quality in 7 studies. In conclusion, our findings confirm that EUS-GBD in dMBO is a feasible, effective, and safe technique as rescue therapy after failure of ERCP or EUS-BD.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".