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Background: Incomplete reporting about what systematic reviewers did and what they found prevents users of the report from being able to fully interpret the findings and understand the limitations of the underlying evidence. Reporting guidelines such as the PRISMA statement and its extensions are designed to improve reporting. However, there are important inconsistencies across the various PRISMA reporting guidelines, which causes confusion and misinterpretation. Coupled with this, users might need to consult multiple guidelines to gain a full understanding of the guidance. Furthermore, the current passive strategy of implementing PRISMA has not fully brought about needed improvements in the completeness of systematic review reporting.
Methods: The PRISMATIC ('PRISMA, Technology, and Implementation to enhance reporting Completeness') project aims to use novel methods to enable more efficient and effective translation of PRISMA reporting guidelines into practice. We will establish a working group who will develop a unified PRISMA statement that harmonises content across the main PRISMA guideline and several of its extensions. We will then develop a web application that generates a reporting template and checklist customised to the characteristics and methods of a systematic review ('PRISMA-Web app') and conduct a randomised trial to evaluate its impact on authors' reporting. We will also develop a web application that helps peer reviewers appraise systematic review manuscripts ('PRISMA-Peer app') and conduct a diagnostic accuracy study to evaluate its impact on peer reviewers' detection of incomplete reporting.
Discussion: We anticipate the novel guidance and web-based apps developed throughout the project will substantively enhance the completeness of reporting of systematic reviews of health evidence, ultimately benefiting users who rely on systematic reviews to inform health care decision-making.
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http://dx.doi.org/10.1186/s13643-023-02363-6 | DOI Listing |
J Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
J Med Internet Res
September 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Mental and behavioral disorders affect approximately 28% of the adult population in Germany per year, with treatment being provided through a diverse health care system. Yet there are access and capacity problems in outpatient mental health care. One innovation that could help reduce these barriers and improve the current state of care is the use of mobile health (mHealth) apps, known in Germany as Digitale Gesundheitsanwendungen (DiGA).
View Article and Find Full Text PDFJMIR Med Inform
September 2025
Department of Hepatobiliary and Vascular Surgery, First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Background: Primary liver cancer, particularly hepatocellular carcinoma (HCC), poses significant clinical challenges due to late-stage diagnosis, tumor heterogeneity, and rapidly evolving therapeutic strategies. While systematic reviews and meta-analyses are essential for updating clinical guidelines, their labor-intensive nature limits timely evidence synthesis.
Objective: This study proposes an automated literature screening workflow powered by large language models (LLMs) to accelerate evidence synthesis for HCC treatment guidelines.
JMIR Hum Factors
September 2025
Department of Music, Arts and Culture Studies, Centre of Excellence in Music, Mind, Body and Brain, University of Jyväskylä, Seminaarinkatu 15, Jyväskylän yliopisto, Jyväskylä, 40014, Finland, 358 6643034.
Background: Personalized Interactive Music Systems (PIMSs) are emerging as promising devices for enhancing physical activity and exercise outcomes. By leveraging real-time data and adaptive technologies, PIMSs align musical features, such as tempo and genre, with users' physical activity patterns, including frequency and intensity, enhancing their overall experience.
Objective: This exploratory systematic review and meta-analysis evaluates the effectiveness of PIMSs across physical, psychophysical, and affective domains.
JBJS Rev
September 2025
Joondalup Health Campus, Joondalup, Australia.
Background: Postoperative swelling is a common complication after total knee arthroplasty (TKA), associated with pain, limited mobility, and delayed recovery. This study aimed to systematically review the literature on interventions that reduce postoperative swelling, categorized into preoperative, intraoperative, and postoperative phases.
Methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-compliant search of PubMed, Medline, Embase, and Cochrane databases was performed for clinical studies evaluating interventions to reduce swelling after primary TKA.