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Ryo, Masahiro; Jeschke, Jonathan M.; Rillig, Matthias C.; Heger, Tina – Research Synthesis Methods, 2020
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel…
Descriptors: Artificial Intelligence, Case Studies, Biology, Research Reports
Moustgaard, Helene; Jones, Hayley E.; Savovic, Jelena; Clayton, Gemma L.; Sterne, Jonathan AC; Higgins, Julian PT; Hróbjartsson, Asbjørn – Research Synthesis Methods, 2020
Randomized clinical trials underpin evidence-based clinical practice, but flaws in their conduct may lead to biased estimates of intervention effects and hence invalid treatment recommendations. The main approach to the empirical study of bias is to collate a number of meta-analyses and, within each, compare the results of trials with and without…
Descriptors: Epidemiology, Evidence, Medical Research, Intervention
Langlois, Alexis; Nie, Jian-Yun; Thomas, James; Hong, Quan Nha; Pluye, Pierre – Research Synthesis Methods, 2018
Objective: Identify the most performant automated text classification method (eg, algorithm) for differentiating empirical studies from nonempirical works in order to facilitate systematic mixed studies reviews. Methods: The algorithms were trained and validated with 8050 database records, which had previously been manually categorized as…
Descriptors: Mixed Methods Research, Databases, Information Retrieval, Search Strategies