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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Bibliometrics, Evidence-Based Medicine, Meta-Analysis as Topic, Publication Bias, Review Literature as Topic, Treatment Outcome.
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Is the systematic review and bibliometric network analysis ( SeBriNA ) a new method to contextualize evidence . Part 1 : description? Context: We describe a new methodology, the systematic review and bibliometric network analysis (SeBriNA), to contextualize the quality and quantity of patient-centered outcomes evidence relative to complementary documents such as reviews, practice guidelines, editorials, and media reports. The SeBriNA is informed by systematic review and bibliometric analysis methodologies. It focuses on two key concepts: 1) quality of evidence for patient-centered outcomes using cumulative meta-analysis and the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) appraisal approach; 2) quantity of original research and its citation relationships to related documents. It includes four steps: 1) research questions and document selection; 2) data extraction and analysis; 3) document network relationships; and 4) document network visualization. The primary output from the SeBriNA is an analysis of 1) evidence-the annual cumulative meta-analysis estimate of effect juxtaposed against quality of evidence by patient-centered outcomes (GRADE), and 2) context-the network of relationships between related documents and original research. This analysis can be represented as a single figure.
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The SeBriNA may help decision makers conceptualize, interpret, and visualize the quantity, quality, and relevance of original research within a network of related documents. Applications include prospective support for clinical and policy decisions and identification of research gaps. Final Decision: yes