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Danielle Pollock; Timothy Hugh Barker; Jennifer C. Stone; Edoardo Aromataris; Miloslav Klugar; Anna M. Scott; Cindy Stern; Amanda Ross-White; Ashley Whitehorn; Rick Wiechula; Larissa Shamseer; Zachary Munn – Research Synthesis Methods, 2024
Predatory journals are a blemish on scholarly publishing and academia and the studies published within them are more likely to contain data that is false. The inclusion of studies from predatory journals in evidence syntheses is potentially problematic due to this propensity for false data to be included. To date, there has been little exploration…
Descriptors: Periodicals, Deception, Ethics, Medical Research
Brinley N. Zabriskie; Nolan Cole; Jacob Baldauf; Craig Decker – Research Synthesis Methods, 2024
Meta-analyses have become the gold standard for synthesizing evidence from multiple clinical trials, and they are especially useful when outcomes are rare or adverse since individual trials often lack sufficient power to detect a treatment effect. However, when zero events are observed in one or both treatment arms in a trial, commonly used…
Descriptors: Meta Analysis, Error Correction, Computation, Simulation
Sandra McKeown; Zuhaib M. Mir – Research Synthesis Methods, 2024
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…
Descriptors: Literature Reviews, Evaluation, Followup Studies, Automation
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
Lorna Wheaton; Dan Jackson; Sylwia Bujkiewicz – Research Synthesis Methods, 2024
During drug development, evidence can emerge to suggest a treatment is more effective in a specific patient subgroup. Whilst early trials may be conducted in biomarker-mixed populations, later trials are more likely to enroll biomarker-positive patients alone, thus leading to trials of the same treatment investigated in different populations. When…
Descriptors: Patients, Drug Therapy, Pharmacology, Outcomes of Treatment
Jona Lilienthal; Sibylle Sturtz; Christoph Schürmann; Matthias Maiworm; Christian Röver; Tim Friede; Ralf Bender – Research Synthesis Methods, 2024
In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are…
Descriptors: Bayesian Statistics, Meta Analysis, Health Sciences, Technology
Hanan Khalil; Danielle Pollock; Patricia McInerney; Catrin Evans; Erica B. Moraes; Christina M. Godfrey; Lyndsay Alexander; Andrea Tricco; Micah D. J. Peters; Dawid Pieper; Ashrita Saran; Daniel Ameen; Petek Eylul Taneri; Zachary Munn – Research Synthesis Methods, 2024
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…
Descriptors: Automation, Computer Software, Synthesis, Protocol Analysis