5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Diabetes as a Prognostic Factor in Frozen Shoulder: A Systematic Review

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          HIGHLIGHTS

          • People with diabetes may experience worse outcomes from frozen shoulder than those without diabetes; however, the certainty in evidence was moderate to low.

          • If high-quality studies can confirm the findings of this review, then clinicians should monitor patients with frozen shoulder with diabetes more closely and offer further treatment if pain or lack of function persists long-term.

          Abstract

          Objective

          To summarize evidence from longitudinal observational studies to determine whether diabetes (types 1 and 2) is associated with the course of symptoms in people with frozen shoulder.

          Data Sources

          A systematic literature search of 11 bibliographic databases (published through June 2021), reference screening, and emailing professional contacts.

          Study Selection

          Studies were selected if they had a longitudinal observational design that included people diagnosed with frozen shoulder at baseline and compared outcomes at follow-up (>2wk) among those with and without diabetes at baseline.

          Data Extraction

          Data extraction was completed by 1 reviewer using a predefined extraction sheet and was checked by another reviewer. Two reviewers independently judged risk of bias using the Quality in Prognostic Factor Studies tool.

          Data Synthesis

          A narrative synthesis, including inspection of forest plots and use of the prognostic factor Grading of Recommendations, Assessment, Development and Evaluations framework. Twenty-eight studies satisfied the inclusion criteria. Seven studies were judged to be at a moderate risk of bias and 21 at a high risk of bias. Diabetes was associated with worse multidimensional clinical scores (moderate certainty in evidence), worse pain (low certainty in evidence), and worse range of motion (very low certainty in evidence).

          Conclusions

          This review provides preliminary evidence to suggest that people with diabetes may experience worse outcomes from frozen shoulder than those without diabetes. If high-quality studies can confirm the findings of this review, then clinicians should monitor patients with frozen shoulder with diabetes more closely and offer further treatment if pain or lack of function persists long-term.

          Related collections

          Most cited references48

          • Record: found
          • Abstract: found
          • Article: not found

          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

          David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Assessing bias in studies of prognostic factors.

            Previous work has identified 6 important areas to consider when evaluating validity and bias in studies of prognostic factors: participation, attrition, prognostic factor measurement, confounding measurement and account, outcome measurement, and analysis and reporting. This article describes the Quality In Prognosis Studies tool, which includes questions related to these areas that can inform judgments of risk of bias in prognostic research.A working group comprising epidemiologists, statisticians, and clinicians developed the tool as they considered prognosis studies of low back pain. Forty-three groups reviewing studies addressing prognosis in other topic areas used the tool and provided feedback. Most reviewers (74%) reported that reaching consensus on judgments was easy. Median completion time per study was 20 minutes; interrater agreement (κ statistic) reported by 9 review teams varied from 0.56 to 0.82 (median, 0.75). Some reviewers reported challenges making judgments across prompting items, which were addressed by providing comprehensive guidance and examples. The refined Quality In Prognosis Studies tool may be useful to assess the risk of bias in studies of prognostic factors.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations

              Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.
                Bookmark

                Author and article information

                Contributors
                Journal
                Arch Rehabil Res Clin Transl
                Arch Rehabil Res Clin Transl
                Archives of Rehabilitation Research and Clinical Translation
                Elsevier
                2590-1095
                14 July 2021
                September 2021
                14 July 2021
                : 3
                : 3
                : 100141
                Affiliations
                [0001]Primary Care Centre Versus Arthritis, School of Medicine, Keele University, Staffordshire, United Kingdom
                Author notes
                [* ]Corresponding author Brett P. Dyer, MSc, Primary Care Centre Versus Arthritis, School of Medicine, David Weatherall building, University Rd, Keele University, Staffordshire, ST5 5BG, UK. b.p.dyer@ 123456keele.ac.uk
                Article
                S2590-1095(21)00051-3 100141
                10.1016/j.arrct.2021.100141
                8463473
                34589691
                ea01393d-6698-464c-a44b-38b4f36d87cd
                © 2021 Published by Elsevier Inc. on behalf of American Congress of Rehabilitation Medicine.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Systematic Review

                adhesive capsulitis,diabetes,frozen shoulder,prognosis,rehabilitation

                Comments

                Comment on this article