MétaCan
Menu
Back to cohort
Record W4408895381 · doi:10.1055/s-0045-1806576

Predictors of Fluoroscopy Exposure During Endoscopic Retrograde Cholangiopancreatography

2025· article· en· W4408895381 on OpenAlexaff
Khaled Khalaf, S Nasruddin, Krystian Pawlak, Monireh Mahjoob, MAG A Bucheeri, Hecheng Li, Christopher Teshima, G May, J. Mosko, Nauzer Forbes, Natalia Causada Calo

Bibliographic record

VenueEndoscopy · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsUniversity of TorontoUniversity of CalgarySt. Michael's Hospital
Fundersnot available
KeywordsMedicineEndoscopic retrograde cholangiopancreatographyFluoroscopyRadiologyEndoscopySurgeryPancreatitis

Abstract

fetched live from OpenAlex

Aims Endoscopic retrograde cholangiopancreatography (ERCP) is a well-established procedure for treating pancreaticobiliary conditions. However, it relies on fluoroscopy use, exposing both the patient and the medical team to ionizing radiation. Understanding the factors associated with prolonged fluoroscopy time—used as an indicator of radiation dose—may improve safety practices and inform patient care. This study aims to identify patient, endoscopist, and institutional factors that predict prolonged fluoroscopy time during ERCP. Methods Data was obtained from a prospective, multi-center, high-fidelity ERCP registry. Patients undergoing ERCP at participating centers provided consent for voluntary enrollment. For each procedure, we documented patient, endoscopist, institutional, and peri-procedural variables. We defined prolonged fluoroscopy time to be≥2 minutes[NF1]. To identify predictors of prolonged fluoroscopy time, we employed stepwise multivariable logistic regression modeling. Statistical significance was set at a p-value of<0.05. Results 4,808 ERCPs were included in our multivariable logistic regression model. Following backwardsselection of variables, our final regression model included a total of 36 variables.Variables associated with increased odds of fluoroscopy time≥2 minutes include the following;trainee involvement (odds ratio, OR, 2.55, 95% confidence interval, CI, 2.20 – 2.97), stricturedilation performed (OR 2.15, 95% CI 1.57 – 2.97), biliary stent placement (OR 2.10, 95% CI1.77 – 2.49), use of double guidewire technique (OR 1.88, 95% CI 1.41 – 2.52), cytologybrushings performed (OR 1.88, 95% CI 1.47 – 2.40), prior sphincterotomy (OR 1.87, 95% CI1.54 – 2.27), presence of a proximal stricture (OR 1.87, 95% CI 1.50 – 2.33), 3-5 CBDcannulation attempts (OR 1.87, 95% CI 1.58 – 2.22), 6-10 CBD cannulation attempts (OR 3.11,95% CI 2.44 – 3.99), &gt;10 CBD cannulation attempts (OR 6.81, 95% CI 4.99 – 9.40),cholangioscpoy performed (OR 1.75, 95% CI 1.08 – 2.92), outpatient setting (OR 1.26, 95% CI1.08 – 1.46), age (OR 1.01, 95% CI 1.01 – 1.01). Conversely, variables associated with lowerodds of fluoroscopy time≥2 minutes include; ERCP performed by female endoscopists (OR0.51, 95% CI 0.40 – 0.65), presence of institutional fluoroscopy training (OR 0.62, 95% CI 0.48– 0.80), presence of a distal stricture (OR 0.66, 95% CI 0.54 – 0.82), fluoroscopy controlled by aradiology technician (OR 0.73, 95% CI 0.59 – 0.90), cholodecholithiasis as the indication (OR0.77, 95% CI 0.64-0.92), Stent-related indications (OR 0.77, 95% CI 0.62 – 0.95), andendoscopist experience (OR 0.99, 95% CI 0.96 – 1.00). Conclusions Fluoroscopy duration during ERCP appears to be reduced in cases performed by female endoscopists and in endoscopists with more experience. Additionally, implementing formal fluoroscopy training and utilizing radiology technicians may reduce fluoroscopy exposure during ERCP. 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)0.001
metaresearch head score (Gemma)0.016
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.258
Teacher spread0.253 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractno

Explore more

Same venueEndoscopySame topicRadiation Dose and ImagingFrench-language works237,207