1. Evaluating characteristics of PROSPERO records as predictors of eventual publication of non-Cochrane systematic reviews: a meta-epidemiological study protocol.
- Author
-
Ruano, Juan, Gómez-García, Francisco, Gay-Mimbrera, Jesús, Aguilar-Luque, Macarena, Fernández-Rueda, José Luis, Fernández-Chaichio, Jesús, Alcalde-Mellado, Patricia, Carmona-Fernandez, Pedro J., Sanz-Cabanillas, Juan Luis, Viguera-Guerra, Isabel, Franco-García, Francisco, Cárdenas-Aranzana, Manuel, Romero, José Luis Hernández, Gonzalez-Padilla, Marcelino, Isla-Tejera, Beatriz, and Garcia-Nieto, Antonio Velez
- Subjects
MACHINE learning ,DEEP learning ,TEXT mining - 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 , andabandoned review subgroups. All articles derived frompublished 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. [ABSTRACT FROM AUTHOR]- Published
- 2018
- Full Text
- View/download PDF