Evaluating the Accuracy and Design of Visual Abstracts in Academic Surgical Journals. Issue 5 (8th November 2022)

• Record Type: Journal Article
• Authors: Guidolin, Keegan; Lin, Justin; Zorigtbaatar, Anudari; Nadeem, Minahil; Ibrahim, Tarek; Neilson, Zdenka; Kim, Kyung Young (Peter); Rajendran, Luckshi; Chadi, Sami; Quereshy, Fayez
• Is Part Of: Annals of surgery. Volume 276:Issue 5(2022)
• Page Start: e275
• Page End: e283
• Publication Date: 2022-11-08
• Subjects: visual abstracts -- health design -- scientific communication -- abstract -- surgical journal; Surgery -- Periodicals; 617.005
• DOI: 10.1097/SLA.0000000000005521
• Languages: English
• ISSNs: 0003-4932

Abstract

Abstract : Objective: The objective of this study was to assess the quality and accuracy of visual abstracts published in academic surgical journals. Background: Visual abstracts are commonly used to disseminate medical research findings. They distill the key messages of a research article, presenting them graphically in an engaging manner so that potential readers can decide whether to read the complete manuscript. Methods: We developed the Visual Abstract Assessment Tool based upon published guidelines. Seven reviewers underwent iterative training to apply the tool. We collected visual abstracts published by 25 surgical journals from January 2017 to April 2021; those corresponding to systematic reviews without meta-analysis, conference abstracts, narrative reviews, video abstracts, or nonclinical research were excluded. Included visual abstracts were scored on accuracy (as compared with written abstracts) and design, and were given a "first impression" score. Results: Across 25 surgical journals 1325 visual abstracts were scored. We found accuracy deficits in the reporting of study design (35.8%), appropriate icon use (49%), and sample size reporting (69.2%), and design deficits in element alignment (54.8%) and symmetry (36.1%). Overall scores ranged from 9 to 14 (out of 15), accuracy scores from 4 to 8 (out of 8), and design scores from 3 to 7 (out of 7). No predictors of visual abstract score were identified. Conclusion: Visual abstracts vary widely in quality. As visual abstracts become integrated with the traditional components of scientific publication, they must be held to similarly high standards. We propose a checklist to be used by authors and journals to standardize the quality of visual abstracts.