Stent misdeployment and factors associated with failure in endoscopic ultrasound-guided choledochoduodenostomy: analysis of the combined datasets from two randomized trials
Bibliographic record
Abstract
BACKGROUND: Stent misdeployment (SMD) is a feared and poorly characterized technical challenge of endoscopic ultrasound (EUS)-guided choledochoduodenostomy (CDS) using lumen-apposing stents. We aimed to ascertain the rate of stent misdeployment in EUS-CDS for malignant distal biliary obstruction (MDBO) and describe its outcomes while identifying variables associated with its occurrence. METHOD: This was a post hoc analysis of two randomized controlled trials comparing EUS-CDS vs. endoscopic retrograde cholangiopancreatography in MDBO. The primary end point was rate of SMD, classified as misdeployment of the distal flange (type I), proximal flange (type II), contralateral bile duct wall injury (type III), or double mucosal puncture (type IV). Multivariable analysis was performed to identify variables associated with SMD and/or technical failure, and with clinical failure or stent dysfunction. RESULTS: 152 patients were included. Technical success was 93.4 %. SMD occurred in 11 patients (7.2 %; 95 %CI 3.1 %-11.4 %): 8 type I, 1 type II, and 2 type III. Endoscopic salvage of SMD was successful in 81.8 %. Misdeployment led to adverse events in four patients (two mild, two moderate), giving an overall SMD-related adverse event rate of 2.6 % (95 %CI 0.7 %-6.6 %). On multivariable analysis, extrahepatic bile duct diameter of ≤ 15 mm was associated with increased odds of SMD and/or technical failure. CONCLUSION: SMD was relatively common in EUS-CDS and was associated with an extrahepatic bile duct diameter of ≤ 15 mm. The majority of misdeployments could be rescued endoscopically with low risk for adverse events.
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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 | 0.036 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.019 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".