EUS-guided biliary drainage in malignant distal biliary obstruction: An international survey to identify barriers of technology implementation
Bibliographic record
Abstract
Background and Objectives: EUS-guided biliary drainage (EUS-BD) is a promising alternative to ERCP in malignant distal biliary obstruction (MDBO). Despite accumulating data, however, its application in clinical practice has been impeded by undefined barriers. This study aims to evaluate the practice of EUS-BD and its barriers. Methods: An online survey was generated using Google Forms. Six gastroenterology/endoscopy associations were contacted between July 2019 and November 2019. Survey questions measured participant characteristics, EUS-BD in different clinical scenarios, and potential barriers. The primary outcome was the uptake of EUS-BD as a first-line modality, without previous ERCP attempts, in patients with MDBO. Results: Overall, 115 respondents completed the survey (2.9% response rate). Respondents were from North America (39.2%), Asia (28.6%), Europe (20%), and other jurisdictions (12.2%). Regarding the uptake of EUS-BD as first-line treatment for MDBO, only 10.5% of respondents would consider EUS-BD as a first-line modality regularly. The main concerns were the lack of high-quality data, fear of adverse events, and limited access to EUS-BD dedicated devices. On multivariable analysis, lack of access to EUS-BD expertise was an independent predictor against the use of EUS-BD, odds ratio 0.16 (95% confidence interval, 0.04-0.65). In salvage situations following failed ERCP, most favored EUS-BD (40.9%) over percutaneous drainage (21.7%) in unresectable cancer. In borderline resectable or locally advanced disease, however, most favored the percutaneous approach due to fear of EUS-BD complicating future surgery. Conclusions: EUS-BD has not reached widespread clinical adoption. Identified barriers include lack of high-quality data, fear of adverse events, and lack of access to EUS-BD dedicated devices. Fear of complicating future surgery was also identified as a barrier in potentially resectable disease.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".