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Qusai Khraisha; Sophie Put; Johanna Kappenberg; Azza Warraitch; Kristin Hadfield – Research Synthesis Methods, 2024
Systematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs) could speed up and automate systematic reviews, but their performance in such tasks has yet to be comprehensively evaluated against humans, and no study has tested Generative Pre-Trained…
Descriptors: Peer Evaluation, Research Reports, Artificial Intelligence, Computer Software
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Schmitz, Tom; Bukowski, Mark; Koschmieder, Steffen; Schmitz-Rode, Thomas; Farkas, Robert – Research Synthesis Methods, 2019
Launching biomedical innovations based on clinical demands instead of translating basic research findings to practice reduces the risk that the results will not fit the clinical routine. To realize this type of innovation, a meta-analysis of the body of research is necessary to reveal demand-matching concepts. However, both the data deluge and the…
Descriptors: Innovation, Biomedicine, Medical Research, Meta Analysis
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Leahy, Joy; O'Leary, Aisling; Afdhal, Nezam; Gray, Emma; Milligan, Scott; Wehmeyer, Malte H.; Walsh, Cathal – Research Synthesis Methods, 2018
The use of individual patient data (IPD) in network meta-analysis (NMA) is becoming increasingly popular. However, as most studies do not report IPD, most NMAs are performed using aggregate data for at least some, if not all, of the studies. We investigate the benefits of including varying proportions of IPD studies in an NMA. Several models have…
Descriptors: Patients, Medical Research, Meta Analysis, Network Analysis