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Gerald Gartlehner; Leila Kahwati; Rainer Hilscher; Ian Thomas; Shannon Kugley; Karen Crotty; Meera Viswanathan; Barbara Nussbaumer-Streit; Graham Booth; Nathaniel Erskine; Amanda Konet; Robert Chew – Research Synthesis Methods, 2024
Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and usability. With the release of large language models (LLMs), new possibilities have emerged to…
Descriptors: Data Collection, Evidence, Synthesis, Language Processing
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Hong, Quan Nha; Bangpan, Mukdarut; Stansfield, Claire; Kneale, Dylan; O'Mara-Eves, Alison; Grootel, Leonie; Thomas, James – Research Synthesis Methods, 2022
Reviewing complex interventions is challenging because they include many elements that can interact dynamically in a nonlinear manner. A systems perspective offers a way of thinking to help understand complex issues, but its application in evidence synthesis is not established. The aim of this project was to understand how and why systems…
Descriptors: Intervention, Systems Approach, Evidence, Synthesis
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Shaw, Liz; Nunns, Michael; Briscoe, Simon; Anderson, Rob; Thompson Coon, Jo – Research Synthesis Methods, 2021
The short time frame associated with rapid reviews can be challenging for researchers conducting qualitative evidence synthesis. In these circumstances a Best-Fit Framework Synthesis, drawing upon existing theory and/or research, may be conducted to rapidly make sense of qualitative evidence. This article discusses a "Rapid Best-Fit"…
Descriptors: Synthesis, Qualitative Research, Evidence, Literature Reviews
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Daly, Caitlin H.; Maconachie, Ross; Ades, A. E.; Welton, Nicky J. – Research Synthesis Methods, 2022
Randomised controlled trials of cancer treatments typically report progression free survival (PFS) and overall survival (OS) outcomes. Existing methods to synthesise evidence on PFS and OS either rely on the proportional hazards assumption or make parametric assumptions which may not capture the diverse survival curve shapes across studies and…
Descriptors: Nonparametric Statistics, Randomized Controlled Trials, Evidence, Networks
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van Valkenhoef, Gert; Dias, Sofia; Ades, A. E.; Welton, Nicky J. – Research Synthesis Methods, 2016
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,…
Descriptors: Networks, Meta Analysis, Automation, Models