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Record W4395053070 · doi:10.1055/s-0044-1782968

Establishing Standards for Gastrointestinal Endoscopic-Related Fluoroscopy: An International Expert Consensus Using a Modified Delphi Process

2024· article· en· W4395053070 on OpenAlexaff
Kareem Khalaf, Katarzyna M. Pawlak, Douglas G. Adler, Asma Alkandari, Alan Barkun, Todd H. Baron, Robert Bechara, Tyler M. Berzin, Cecilia Binda, Ming‐Yan Cai, Silvia Carrara, Y. I. Chen, Eduardo Guimarães Hourneaux de Moura, Nauzer Forbes, Alessandro Fugazza, Cesare Hassan, Philip James, Michel Kahaleh, Harry Martin, Roberta Maselli, Gary R. May, J. Mosko, Ganiyat K. Oyeleke, B Petersen, Alessandro Repici, Payal Saxena, Amrita Sethi, Reem Z. Sharaiha, Marco Spadaccini, Raymond S. Tang, Christopher Teshima, Mariano Villarroel, Jeanin E. van Hooft, Rogier P. Voermans, Daniel von Renteln, Catharine M. Walsh, Tricia Aberin, Dawn Banavage, Jie Chen, James Clancy, Henrik Drake, Mendeleev Im, Chor Ping Low, Alexandra Myszko, Kaela Navarro, Jeremy A. Redman, W. Reyes, F.S. Weinstein, Yoshinori Fujiyoshi, A Mokhtar, C. Na, Daniel Tham, Nikko Gimpaya, Samir C. Grover, Nicolas Calo

Bibliographic record

VenueEndoscopy · 2024
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsQueen's UniversityCentre Hospitalier de l’Université de MontréalUniversity of CalgaryMcGill UniversitySickKids FoundationToronto General HospitalSt. Michael's Hospital
Fundersnot available
KeywordsMedicineFluoroscopyDelphi methodDelphiProcess (computing)Medical physicsEndoscopyRadiologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Aims The use of fluoroscopy in gastrointestinal endoscopic procedures offers valuable insights but also raises concerns about radiation exposure. This study aims to develop evidence-based guidelines for the safe and effective use of fluoroscopy in such procedures, prioritizing the safety of patients and healthcare workers. Methods A modified Delphi method was employed to achieve consensus among 46 experts from six continents. Three rounds of voting were conducted, and consensus was defined as at least 80% agreement. Thirty-one statements achieved consensus in the second round, focusing on patient safety, staff safety, education, pregnancy, and family planning. The statements were rated on a scale of 1 to 10 in the third round. Results In our study, 46 experts, consisting of 34 physicians therapeutic endoscopists and 12 endoscopy nurses, participated from six continents, with 45.6% female representation (n=21). Following three rounds of voting, a total of 43 item statements were generated in the first round, covering various categories including General Considerations, Education, Pregnancy, Family Planning, Patient Safety, and Staff Safety. Out of these, 31 statements achieved consensus after the second round. The accepted statements were categorized into General Considerations (6 questions), Education (10 statements), Pregnancy (4 statements), Family Planning (2 statements), Patient Safety (4 statements), and Staff Safety (5 statements). In the third round, the statements received mean scores ranging from 7.28 to 9.36 on a scale of 1 to 10, with up to 87.18% of responses scoring them as a very high priority. Conclusions This study presents consensus-based standards for the safe use of fluoroscopy in gastrointestinal endoscopic procedures, addressing the well-being of both patients and healthcare workers. These guidelines aim to mitigate the risks associated with radiation exposure while maintaining the benefits of fluoroscopy, ultimately promoting a culture of safety in healthcare settings. Publication History Article published online: 15 April 2024 © 2024. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 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)0.304
metaresearch head score (Gemma)0.241
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.304
Threshold uncertainty score0.858

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3040.241
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0080.004
Science and technology studies0.0040.004
Scholarly communication0.0040.005
Open science0.0030.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.396
Teacher spread0.351 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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