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      Clinical efficacy of Vestibular Incision Subperiosteal Tunnel Access (VISTA) for treatment of multiple gingival recession defects: a systematic review, meta-analysis and meta-regression

      , , , ,
      Clinical Oral Investigations
      Springer Science and Business Media LLC

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          RoB 2: a revised tool for assessing risk of bias in randomised trials

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            Conducting Meta-Analyses inRwith themetaforPackage

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              Grading quality of evidence and strength of recommendations in clinical practice guidelines. Part 1 of 3. An overview of the GRADE approach and grading quality of evidence about interventions.

              The GRADE (Grades of Recommendation, Assessment, Development, and Evaluation) approach provides guidance to grading the quality of underlying evidence and the strength of recommendations in health care. The GRADE system's conceptual underpinnings allow for a detailed stepwise process that defines what role the quality of the available evidence plays in the development of health care recommendations. The merit of GRADE is not that it eliminates judgments or disagreements about evidence and recommendations, but rather that it makes them transparent. This first article in a three-part series describes the GRADE framework in relation to grading the quality of evidence about interventions based on examples from the field of allergy and asthma. In the GRADE system, the quality of evidence reflects the extent to which a guideline panel's confidence in an estimate of the effect is adequate to support a particular recommendation. The system classifies quality of evidence as high, moderate, low, or very low according to factors that include the study methodology, consistency and precision of the results, and directness of the evidence.
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                Author and article information

                Contributors
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                Journal
                Clinical Oral Investigations
                Clin Oral Invest
                Springer Science and Business Media LLC
                1436-3771
                December 2023
                November 27 2023
                : 27
                : 12
                : 7171-7187
                Article
                10.1007/s00784-023-05383-7
                4eb49472-2a6f-4436-ba69-584339bb82c7
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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