Search

Abstract Background Article summaries’ information and structure may influence researchers/clinicians’ decisions to conduct deeper full-text analyses. Specifically, abstracts of systematic reviews (SRs) and meta-analyses (MA) should provide structured summaries for quick assessment. This study explored a method for determining the methodological quality and bias risk of full-text reviews using abstract information alone. Methods Systematic literature searches for SRs and/or MA about psoriasis were undertaken on MEDLINE, EMBASE, and Cochrane database. For each review, quality, abstract-reporting completeness, full-text methodological quality, and bias risk were evaluated using Preferred Reporting Items for Systematic Reviews and Meta-analyses for abstracts (PRISMA-A), Assessing the Methodological Quality of Systematic Reviews (AMSTAR), and ROBIS tools, respectively. Article-, author-, and journal-derived metadata were systematically extracted from eligible studies using a piloted template, and explanatory variables concerning abstract-reporting quality were assessed using univariate and multivariate-regression models. Two classification models concerning SRs’ methodological quality and bias risk were developed based on per-item and total PRISMA-A scores and decision-tree algorithms. This work was supported, in part, by project ICI1400136 (JR). No funding was received from any pharmaceutical company. Results This study analysed 139 SRs on psoriasis interventions. On average, they featured 56.7% of PRISMA-A items. The mean total PRISMA-A score was significantly higher for high-methodological-quality SRs than for moderate- and low-methodological-quality reviews. SRs with low-bias risk showed higher total PRISMA-A values than reviews with high-bias risk. In the final model, only ’authors per review > 6’ (OR: 1.098; 95%CI: 1.012-1.194), ’academic source of funding’ (OR: 3.630; 95%CI: 1.788-7.542), and ’PRISMA-endorsed journal’ (OR: 4.370; 95%CI: 1.785-10.98) predicted PRISMA-A variability. Reviews with a total PRISMA-A score

Abstract Background Epidemiology and the reporting characteristics of systematic reviews (SRs) and meta-analyses (MAs) are well known. However, no study has analyzed the influence of protocol features on the probability that a study’s results will be finally reported, thereby indirectly assessing the reporting bias of International Prospective Register of Systematic Reviews (PROSPERO) registration records. Objective The objective of this study is to explore which factors are associated with a higher probability that results derived from a non-Cochrane PROSPERO registration record for a systematic review will be finally reported as an original article in a scientific journal. Methods/design The PROSPERO repository will be web scraped to automatically and iteratively obtain all completed non-Cochrane registration records stored from February 2011 to December 2017. Downloaded records will be screened, and those with less than 90% fulfilled or are duplicated (i.e., those sharing titles and reviewers) will be excluded. Manual and human-supervised automatic methods will be used for data extraction, depending on the data source (fields of PROSPERO registration records, bibliometric databases, etc.). Records will be classified into published, discontinued, and abandoned review subgroups. All articles derived from published reviews will be obtained through multiple parallel searches using the full protocol “title” and/or “list reviewers” in MEDLINE/PubMed databases and Google Scholar. Reviewer, author, article, and journal metadata will be obtained using different sources. R and Python programming and analysis languages will be used to describe the datasets; perform text mining, machine learning, and deep learning analyses; and visualize the data. We will report the study according to the recommendations for meta-epidemiological studies adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for SRs and MAs. Discussion This meta-epidemiological study will explore, for the first time, characteristics of PROSPERO records that may be associated with the publication of a completed systematic review. The evidence may help to improve review workflow performance in terms of research topic selection, decision-making regarding team selection, planning relationships with funding sources, implementing literature search strategies, and efficient data extraction and analysis. We expect to make our results, datasets, and R and Python code scripts publicly available during the third quarter of 2018.