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Record W4408901687 · doi:10.1055/s-0045-1805268

Evaluating ERCP Training for Nursing Professionals: Identifying Gaps via an International Survey to Develop a Standardized Curriculum

2025· article· en· W4408901687 on OpenAlexaff
Dawn Banavage, Khaled Khalaf, Daniel Tham, Natalia Causada Calo, John Clancy

Bibliographic record

VenueEndoscopy · 2025
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineCurriculumMedical educationTraining (meteorology)NursingMEDLINEPedagogy

Abstract

fetched live from OpenAlex

ERCP is a complex endoscopic procedure that demands highly skilled nurses and technicians. However, ERCP training faces significant challenges due to the lack of a standardized curriculum, leading to variations in training content, duration, and structure. This often leaves nurses feeling underprepared to manage the procedural and patient care complexities involved. This study aims to assess the current state of ERCP training among nursing professionals, identify key areas for improvement, and establish a foundation for developing a standardized ERCP nursing curriculum. An international survey was conducted from May to September 2024, targeting endoscopic nurses engaged in ERCP across four global regions: Asia, Europe, Oceania, and North America. The survey was distributed electronically to 58 endoscopic nurses, who were invited to share insights on their training regimens, procedural involvement, and perceived levels of preparedness. Data were analyzed quantitatively and qualitatively and the analysis aimed to identify common themes, strengths, and areas for improvement in the training processes [ 1 ]. Over 70% of respondents worked in tertiary care hospitals, with more than 80% performing ERCPs weekly or daily. Additionally, 72.4% reported being able to assist with advanced ERCP techniques, such as cholangioscopy, laser therapy, and biliary radiofrequency ablation. A significant majority, 81%, indicated they utilized common language and standardized communication during procedures, and 77.6% had access to ERCP educational resources. However, only 50% experienced consistent procedural training, and 32.8% found the learning objectives unclear. Furthermore, 51.7% lacked hands-on access to ERCP accessories outside of procedures, and 34.5% received no formal feedback during their training. Key challenges identified included managing complex equipment, understanding hepatobiliary anatomy, achieving technical coordination with physicians, and interpreting fluoroscopic images. Respondents emphasized the importance of clear training objectives, better pre-procedural preparation, and consistent teaching methods. Many also cited a need for pre-procedure learning packages, instructional videos, and additional practice equipment to enhance their preparation. Essential areas of pre-training knowledge highlighted by respondents included anatomy – especially of the biliary tree, clinical indications for ERCP, and familiarity with procedural tools and terminology. Observing experienced staff and accessing pre-training resources were also noted as highly beneficial. Significant training gaps remain in ERCP education, including the need for clearer objectives and intraprocedural communication, improved pre-procedural resources, more hands-on practice, and consistent evaluation. Respondents prioritized GI anatomy, pre-procedure learning materials, and hands-on practice as the top training needs, underscoring the importance of a more structured and standardized approach to ERCP training. 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)0.022
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.113
GPT teacher head0.510
Teacher spread0.397 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
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