PD36 Assigning GRADE Levels To An Overview Of Reviews Using General Principles Identified From Current GRADE Guidelines

Introduction Existing guidelines on overviews of reviews and umbrella reviews recommend an assessment of the certainty of evidence but provide limited guidance on how to apply GRADE to such a complex evidence synthesis. We present one approach to applying GRADE to an overview of reviews developed using general principles derived from current GRADE guidelines. Methods The methods were developed in an iterative and exploratory fashion following discussion with 11 methodologists and health services researchers. Key principles were distilled on the five GRADE domains (risk of bias, inconsistency, imprecision, indirectness, and publication bias) from the relevant GRADE guidelines, particularly those on test accuracy. Results A ‘general principles’ approach of applying the five domains of GRADE to an overview of reviews and arriving at an overall summary judgment for outcomes was developed. These methods were successfully applied to an overview of reviews on 18F-prostate specific membrane antigen positron emission tomography and computed tomography in the staging of patients with high-risk or recurrent prostate cancer. Conclusions Our approach distilled key principles from relevant GRADE guidelines and allowed us to apply GRADE to a complex body of evidence. Such an approach may be of interest to other researchers working on overviews of reviews or umbrella reviews.

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Publication Details

Journal
International Journal of Technology Assessment in Health Care
Published
2024-12-01
DOI
https://doi.org/10.1017/s026646232400299x
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

PD36 Assigning GRADE Levels To An Overview Of Reviews Using General Principles Identified From Current GRADE Guidelines

Kirsty O’Brien, M. McGarry, Kieran Walsh, Lydia O’Sullivan et al.
International Journal of Technology Assessment in Health Care
Radiomics and Machine Learning in Medical Imaging
article

PD36 Assigning GRADE Levels To An Overview Of Reviews Using General Principles Identified From Current GRADE Guidelines

Kirsty O’Brien, M. McGarry, Kieran Walsh, Lydia O’Sullivan, Andrew Dullea, Susan Ahern, Máirín Ryan, Patricia Harrington, Susan M. Smith
article en

Abstract

Introduction Existing guidelines on overviews of reviews and umbrella reviews recommend an assessment of the certainty of evidence but provide limited guidance on how to apply GRADE to such a complex evidence synthesis. We present one approach to applying GRADE to an overview of reviews developed using general principles derived from current GRADE guidelines. Methods The methods were developed in an iterative and exploratory fashion following discussion with 11 methodologists and health services researchers. Key principles were distilled on the five GRADE domains (risk of bias, inconsistency, imprecision, indirectness, and publication bias) from the relevant GRADE guidelines, particularly those on test accuracy. Results A ‘general principles’ approach of applying the five domains of GRADE to an overview of reviews and arriving at an overall summary judgment for outcomes was developed. These methods were successfully applied to an overview of reviews on 18F-prostate specific membrane antigen positron emission tomography and computed tomography in the staging of patients with high-risk or recurrent prostate cancer. Conclusions Our approach distilled key principles from relevant GRADE guidelines and allowed us to apply GRADE to a complex body of evidence. Such an approach may be of interest to other researchers working on overviews of reviews or umbrella reviews.

International Journal of Technology Assessment in Health CareVol. 40(S1)
Openalex Percentile: Top 58%
Radiomics and Machine Learning in Medical Imaging
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PD36 Assigning GRADE Levels To An Overview Of Reviews Using General Principles Identified From Current GRADE Guidelines — Kirsty O’Brien, M. McGarry, et al. · International Journal of Technology Assessment in Health Care (2024) | TGRS Research Map | TGRS