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      Comparison of robotic‑assisted versus conventional laparoscopic surgery for mid–low rectal cancer: a systematic review and meta-analysis

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          Is Open Access

          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Effect of Robotic-Assisted vs Conventional Laparoscopic Surgery on Risk of Conversion to Open Laparotomy Among Patients Undergoing Resection for Rectal Cancer

            Robotic rectal cancer surgery is gaining popularity, but limited data are available regarding safety and efficacy.
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              RECURRENCE AND SURVIVAL AFTER TOTAL MESORECTAL EXCISION FOR RECTAL CANCER

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                Author and article information

                Journal
                Journal of Cancer Research and Clinical Oncology
                J Cancer Res Clin Oncol
                Springer Science and Business Media LLC
                0171-5216
                1432-1335
                November 2023
                August 14 2023
                November 2023
                : 149
                : 16
                : 15207-15217
                Article
                10.1007/s00432-023-05228-6
                7f9aa766-ad86-4a73-9914-2740a9a3bf1d
                © 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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