We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified relevant for full-text review were used for the analysis. Of these, 40% (n = 6573) were sub-divided for…

Searching multiple resources to locate eligible studies for research syntheses can result in hundreds to thousands of duplicate references that should be removed before the screening process for efficiency. Research investigating the performance of automated methods for deduplicating references via reference managers and systematic review software…

Objective: This paper describes several automation tools and software that can be considered during evidence synthesis projects and provides guidance for their integration in the conduct of scoping reviews. Study Design and Setting: The guidance presented in this work is adapted from the results of a scoping review and consultations with the JBI…

Systematic reviews are resource-intensive. The machine learning tools being developed mostly focus on the study identification process, but tools to assist in analysis and categorization are also needed. One possibility is to use unsupervised automatic text clustering, in which each study is automatically assigned to one or more meaningful…

Systematic reviews are vital instruments for researchers to understand broad trends in a field and synthesize evidence on the effectiveness of interventions in addressing specific issues. The quality of a systematic review depends critically on having comprehensively surveyed all relevant literature on the review topic. In addition to database…

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or…

When performing a systematic review, researchers screen the articles retrieved after a broad search strategy one by one, which is time-consuming. Computerised support of this screening process has been applied with varying success. This is partly due to the dependency on large amounts of data to develop models that predict inclusion. In this…

Few data-driven approaches are available to estimate the risk of conclusion change in systematic review updates. We developed a rule-based approach to automatically extract information from reviews and updates to be used as features for modelling conclusion change risk. Rules were developed to extract relevant information from published Cochrane…

We present the R package and web app "statcheck" to automatically detect statistical reporting inconsistencies in primary studies and meta-analyses. Previous research has shown a high prevalence of reported p-values that are inconsistent--meaning a re-calculated p-value, based on the reported test statistic and degrees of freedom, does…

The Systematic Review Toolbox aims provide a web-based catalogue of tools that support various tasks within the systematic review and wider evidence synthesis process. Identifying publications surrounding specific systematic review tools is currently challenging, leading to a high screening burden for few eligible records. We aimed to develop a…

Approaches to synthesizing qualitative data have, to date, largely focused on integrating the findings from published reports. However, developments in text mining software offer the potential for efficient analysis of large pooled primary qualitative datasets. This case study aimed to (a) provide a step-by-step guide to using one software…

Systematic reviews are a cornerstone of today's evidence-informed decision making. With the rapid expansion of questions to be addressed and scientific information produced, there is a growing workload on reviewers, making the current practice unsustainable without the aid of automation tools. While many automation tools have been developed and…

Network meta-analysis (NMA) is gaining popularity for comparing multiple treatments in a single analysis. Generalized linear mixed models provide a unifying framework for NMA, allow us to analyze datasets with dichotomous, continuous or count endpoints, and take into account multiarm trials, potential heterogeneity between trials and network…

Network meta-analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node-splitting approach,…