Background: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement aims to optimize the reporting of systematic reviews. The performance of the PRISMA Statement in improving the reporting and quality of surgical systematic reviews remains unclear.
Methods: Systematic reviews published in five high-impact surgical journals between 2007 and 2015 were identified from online archives. Manuscripts blinded to journal, publication year and authorship were assessed according to 27 reporting criteria described by the PRISMA Statement and scored using a validated quality appraisal tool (AMSTAR, Assessing the Methodological Quality of Systematic Reviews). Comparisons were made between studies published before (2007-2009) and after (2011-2015) its introduction. The relationship between reporting and study quality was measured using Spearman's rank test.
Results: Of 281 eligible manuscripts, 80 were published before the PRISMA Statement and 201 afterwards. Most manuscripts (208) included a meta-analysis, with the remainder comprising a systematic review only. There was no meaningful change in median compliance with the PRISMA Statement (19 (i.q.r. 16-21) of 27 items before versus 19 (17-22) of 27 after introduction of PRISMA) despite achieving statistical significance (P = 0·042). Better reporting compliance was associated with higher methodological quality (r = 0·70, P < 0·001).
Conclusion: The PRISMA Statement has had minimal impact on the reporting of surgical systematic reviews. Better compliance was associated with higher-quality methodology.
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http://dx.doi.org/10.1002/bjs.10423 | DOI Listing |
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Cancer Screening, American Cancer Society, Atlanta, GA, United States.
Background: The online nature of decision aids (DAs) and related e-tools supporting women's decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs.
Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy.
Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Background: Lifestyle interventions have been acknowledged as effective strategies for preventing type 2 diabetes mellitus (T2DM). However, the accessibility of conventional face-to-face interventions is often limited. Digital health intervention has been suggested as a potential solution to overcome the limitation.
View Article and Find Full Text PDFCien Saude Colet
January 2025
Departamento de Enfermagem, Universidade Federal de Sergipe. Aracaju SE Brasil.
This review aimed to identify the impact of the ECHO® model on monitoring people diagnosed with diabetes mellitus. It followed the Joanna Briggs Institute and the PRISMA-ScR Checklist. The search was conducted in the Cochrane Library, Embase, Virtual Health Library, PubMed/MEDLINE, Scopus, and Web of Science databases.
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