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Record W4408901774 · doi:10.1055/s-0045-1805305

Standardizing EUS-guided Gastroenterostomy: a Delphi consensus on technical steps and adverse events management

2025· article· en· W4408901774 on OpenAlexaff
Giuseppe Vanella, Riccardo Leone, F Frigo, Michiel Bronswijk, Roy L.J. van Wanrooij, Yen‐I Chen, Kenneth F. Binmoeller, Manuel Pérez‐Miranda, P Chalal, Manol Jovani, Amy Tyberg, Enrique Pérez‐Cuadrado‐Robles, Marc Barthet, Pierre H. Deprez, Michel Kahaleh, Douglas G. Adler, M Khashab, Anthony Yuen Bun Teoh, Takao Itoi, Sandeep Lakhtakia, Rastislav Kunda, Van der Merwe, Paolo Giorgio Arcidiacono

Bibliographic record

VenueEndoscopy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineGastroenterostomyDelphiDelphi methodGeneral surgeryMEDLINEAdverse effectMedical physicsIntensive care medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Aims EUS-guided gastroenterostomy (EUS-GE) is increasingly used in the management of Gastric Outlet Obstruction. However, significant variability exists in technical choices which might account for heterogeneous clinical outcomes. The aim of this Delphi methodology was to address open questions and to gather expert consensus on key aspects of EUS-GE. Methods A panel of 25 international leading experts in EUS-GE was invited to revise literature around the technique. A Delphi process was conducted over three rounds, with each round involving anonymous voting on 29 predefined statements, using a 5-point Likert scale (1: strongly disagree – 5: totally agree). Responses were analysed through Medians [Interquartile Ranges], with pre-defined thresholds for approval, revision or discard of the statement. Statements reaching final consensus were graded based on the strength of agreement, defined as the proportion of responses rated 4 or 5. Results Response rate was 88% in Round 1 and 100% in Round 2 and 3. Among 29 statements, eight were approved at Round 1, eighteen at Round 2 and four at Round 3, while two were ultimately rejected. There was early and excellent agreement (> 95%) on the need of fluoroscopy for EUS-GE, the need for electrocautery-enhanced Lumen Apposing Metal Stent (LAMS), and the preference for free-hand LAMS release. All panellists agreed that endoscopists performing EUS-GE should be familiar with management of Adverse Events (AEs) such as bleedings or perforations. After discussion, excellent agreement was obtained for the management of AEs (misdeployments and bleedings) and LAMS dysfunctions. Final Strong agreement (> 90%) was reached for preferred patient positioning, the required sedation and the preference for saline solution for jejunal distention. Extensive discussion with final Moderate agreement (> 80%) was reached on the use of dye, the preference for catheter-assisted EUS-GE instead of endoscope- or needle-directed instillation, the typical location for EUS-GE and the operative space required for LAMS release. Statements on the use of contrast and the choice between WEST and EPASS techniques were removed due to lack of agreement. Conclusions Despite technical differences (such as the preference of a jejunal catheter or a double-balloon catheter) most EUS-GE experts agree on key technical principles, providing valuable guidance on the standardization of EUS-GE in clinical practice. Conversely, certain topics show limited agreement, identifying future research priorities in the field of EUS-GE. Publication History Article published online: 27 March 2025 © 2025. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.340
metaresearch head score (Gemma)0.326
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.340
Threshold uncertainty score0.814

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3400.326
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.002
Science and technology studies0.0040.004
Scholarly communication0.0060.005
Open science0.0040.010
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.059
GPT teacher head0.442
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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