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      The association between anxiety and cardiac interoceptive accuracy: A systematic review and meta-analysis

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      Neuroscience & Biobehavioral Reviews
      Elsevier BV

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          PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews

          The methods and results of systematic reviews should be reported in sufficient detail to allow users to assess the trustworthiness and applicability of the review findings. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was developed to facilitate transparent and complete reporting of systematic reviews and has been updated (to PRISMA 2020) to reflect recent advances in systematic review methodology and terminology. Here, we present the explanation and elaboration paper for PRISMA 2020, where we explain why reporting of each item is recommended, present bullet points that detail the reporting recommendations, and present examples from published reviews. We hope that changes to the content and structure of PRISMA 2020 will facilitate uptake of the guideline and lead to more transparent, complete, and accurate reporting of systematic reviews.
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            How to perform a meta-analysis with R: a practical tutorial

            Meta-analysis is of fundamental importance to obtain an unbiased assessment of the available evidence. In general, the use of meta-analysis has been increasing over the last three decades with mental health as a major research topic. It is then essential to well understand its methodology and interpret its results. In this publication, we describe how to perform a meta-analysis with the freely available statistical software environment R, using a working example taken from the field of mental health. R package meta is used to conduct standard meta-analysis. Sensitivity analyses for missing binary outcome data and potential selection bias are conducted with R package metasens. All essential R commands are provided and clearly described to conduct and report analyses. The working example considers a binary outcome: we show how to conduct a fixed effect and random effects meta-analysis and subgroup analysis, produce a forest and funnel plot and to test and adjust for funnel plot asymmetry. All these steps work similar for other outcome types. R represents a powerful and flexible tool to conduct meta-analyses. This publication gives a brief glimpse into the topic and provides directions to more advanced meta-analysis methods available in R.
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              An inventory for measuring clinical anxiety: Psychometric properties.

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                Author and article information

                Journal
                Neuroscience & Biobehavioral Reviews
                Neuroscience & Biobehavioral Reviews
                Elsevier BV
                01497634
                September 2022
                September 2022
                : 140
                : 104754
                Article
                10.1016/j.neubiorev.2022.104754
                35798125
                a3c0abb0-1ada-444b-8632-5b192f160dc2
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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