Lumen-Apposing Metal Stent With and Without Concurrent Double-Pigtail Plastic Stent for Pancreatic Fluid Collections: A Comparative Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Lumen-apposing metal stents (LAMSs) are often used to drain pancreatic fluid collections (PFCs). However, adverse events, such as stent obstruction, infection, or bleeding, have been reported. Concurrent double-pigtail plastic stent (DPPS) deployment has been suggested to prevent these adverse events. This meta-analysis aimed to compare the clinical outcomes of LAMS with DPPS vs. LAMS alone in the drainage of PFCs. Methods: An extensive search was conducted in the literature to include all the eligible studies that compared LAMS with DPPS vs. LAMS alone for drainage of PFCs. Pooled risk ratios (RRs) with the 95% confidence intervals (CIs) were obtained within a random-effect model. The outcomes were technical and clinical success, and overall adverse events, including stent migration and occlusion, bleeding, infection, and perforation. Results: Five studies involving 281 patients with PFCs (137 received LAMS plus DPPS vs. 144 received LAMS alone) were included. LAMS plus DPPS group was associated with comparable technical success (RR: 1.01, 95% CI: 0.97 - 1.04, P = 0.70) and clinical success (RR: 1.01, 95% CI: 0.88 - 1.17). Lower trends of overall adverse events (RR: 0.64, 95% CI: 0.32 - 1.29), stent occlusion (RR: 0.63, 95% CI: 0.27 - 1.49), infection (RR: 0.50, 95% CI: 0.15 - 1.64), and perforation (RR: 0.42, 95% CI: 0.06 - 2.78) were observed in LAMS with DPPS group compared to LAMS alone but without a statistical significance. Stent migration (RR: 1.29, 95% CI: 0.50 - 3.34) and bleeding (RR: 0.65, 95% CI: 0.25 - 1.72) were similar between the two groups. Conclusions: Deployment of DPPS across LAMS for drainage of PFCs has no significant impact on efficacy or safety outcomes. Randomized, controlled trials are necessary to confirm our study results, especially in walled-off pancreatic necrosis.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.005 | 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) | 0.004 | 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".