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Combining Randomized and Non-Randomized Data to Predict Heterogeneous Effects of Competing Treatments
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Konstantina Chalkou; Tasnim Hamza; Pascal Benkert; Jens Kuhle; Chiara Zecca; Gabrielle Simoneau; Fabio Pellegrini; Andrea Manca; Matthias Egger; Georgia Salanti – Research Synthesis Methods, 2024
Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different…
Descriptors: Medical Research, Outcomes of Treatment, Risk, Randomized Controlled Trials
Feasibility and Efficacy of a Virtual Reality Social Prediction Training in Children and Young Adults with Congenital Cerebellar Malformations
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Niccolò Butti; Emilia Biffi; Romina Romaniello; Alessandra Finisguerra; Enza Maria Valente; Sandra Strazzer; Renato Borgatti; Cosimo Urgesi – Journal of Autism and Developmental Disorders, 2025
This study tested the feasibility and efficacy of a Virtual Reality (VR) social prediction training (VR-Spirit) specifically designed for patients with congenital cerebellar malformation. The study is a randomised controlled trial in which 28 cerebellar patients aged 7-25 yo were randomly allocated to the VR-Spirit or to a control intervention in…
Descriptors: Computer Simulation, Prediction, Interpersonal Competence, Children
Employing Machine Learning to Predict Medical Trainees' Psychophysiological Responses and Self- and Socially-Shared Regulated Learning Strategies While Completing Medical Simulations
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Matthew Moreno; Keerat Grewal; Maria Cutumisu; Jason M. Harley – Educational Psychology Review, 2025
Medical simulations allow medical trainees to work within teams to develop their self-regulated learning (SRL) and socially shared regulated learning (SSRL) skills. These skills are imperative in optimizing performance and teamwork and could be reflected in physiological responses given by learners. This study examines how medical trainees'…
Descriptors: Artificial Intelligence, Technology Uses in Education, Prediction, Algorithms
Employing Machine Learning to Predict Medical Trainees' Psychophysiological Responses and Self- and Socially-Shared Regulated Learning Strategies While Completing Medical Simulations
Peer reviewed Peer reviewed
Direct linkDirect link
Matthew Moreno; Keerat Grewal; Maria Cutumisu; Jason M. Harley – Educational Psychology Review, 2025
Medical simulations allow medical trainees to work within teams to develop their self-regulated learning (SRL) and socially shared regulated learning (SSRL) skills. These skills are imperative in optimizing performance and teamwork and could be reflected in physiological responses given by learners. This study examines how medical trainees'…
Descriptors: Artificial Intelligence, Technology Uses in Education, Prediction, Algorithms
Cypriot Healthcare Professionals' Knowledge and Skills When Interacting with Patients with Aphasia in Clinical Settings
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Marina Charalambous; Phivos Phylactou; Marios Serafim; Pinelopi Vlotinou; Anastasios M. Georgiou; Eliada Pampoulou; Maria Papaioannou; Fotini Georgiou; Lakis Palazis – International Journal of Language & Communication Disorders, 2025
Background: Aphasia, a communication disorder mainly resulting from stroke, poses challenges to the meaningful interactions between healthcare professionals and people with aphasia (PWA). Little is known about the knowledge and skills of Cypriot healthcare professionals when interacting with PWA in clinical settings. This study explores the…
Descriptors: Patients, Aphasia, Allied Health Personnel, Speech Therapy