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Published by American Society for Gastrointestinal Endoscopy (ASGE) [2-4] · Expert opinion and group consensus [2, 5]
This guideline from the ASGE proposes 9 quality measures designed to improve the design, conduct, and reporting of endoscopic research [1]. Developed by a joint task force via group consensus and systematic literature searches [2], it establishes a framework covering sample size calculation, patient selection, outcome definitions, data integrity, blinding, trial oversight, equity, and conflict disclosures [1, 2, 6, 8].
Key Takeaways
This document represents the first version (v1) of these quality measures, prioritizing indicators judged to be reasonably practical and achievable. Content is expected to evolve in future versions based on feedback from endoscopic investigators and a changing evidence base.
The study adheres to published reporting guidelines
The sample size is calculated a priori and according to established principles
The study sample reflects the patient population to which the results will apply
Study outcomes and exposures or interventions are clearly defined before study implementation, and are as objective as possible
Data integrity is ensured throughout the data acquisition and verification processes
Blinding in randomized trials is maintained to the greatest extent possible
Randomized trials are conducted under the oversight of an independent data and safety review committee
The research is designed and conducted inclusively and equitably
Commercial and intellectual conflicts of interest are disclosed and managed
Clinical Topic
Quality measures in endoscopic research [1, 2]
Objectives
To propose quality measures that aim to improve the overall quality, rigor, and generalizability of endoscopy and endoscopy-related research [1]
Target Patient Population
Patients undergoing endoscopic procedures [1, 6]
Target Providers
Therapeutic Area
GastroenterologyGuideline Scope
System: Expert opinion and group consensus [2, 5]
Monitoring Guidance
Central monitoring of incoming data to identify trends, duplicate data entry for minimizing risk of errors, and auditing a percentage of medical records for data accuracy and quality assurance [10].
Difficulty
advanced
Learning Paths