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Direct linkKebede, Mihiretu M.; Le Cornet, Charlotte; Fortner, Renée Turzanski – Research Synthesis Methods, 2023
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…
Descriptors: Automation, Literature Reviews, Artificial Intelligence, Algorithms
Bobrovitz, Niklas; Noël, Kim; Li, Zihan; Cao, Christian; Deveaux, Gabriel; Selemon, Anabel; Clifton, David A.; Yanes-Lane, Mercedes; Yan, Tingting; Arora, Rahul K. – Research Synthesis Methods, 2023
Risk of bias (RoB) assessments are a core element of evidence synthesis but can be time consuming and subjective. We aimed to develop a decision rule-based algorithm for RoB assessment of seroprevalence studies. We developed the SeroTracker-RoB algorithm. The algorithm derives seven objective and two subjective critical appraisal items from the…
Descriptors: Decision Making, Algorithms, Risk, Bias
Zhipeng Hou; Elizabeth Tipton – Research Synthesis Methods, 2024
Literature screening is the process of identifying all relevant records from a pool of candidate paper records in systematic review, meta-analysis, and other research synthesis tasks. This process is time consuming, expensive, and prone to human error. Screening prioritization methods attempt to help reviewers identify most relevant records while…
Descriptors: Meta Analysis, Research Reports, Identification, Evaluation Methods
