Objectives: The efficacy and capacity of artificial intelligence models to predict posttransplant health complications have been disputed over the past few years. In this systematic review, we assessed the performance of different artificial intelligence models in predicting health outcomes after heart and lung transplantations.
Materials And Methods: We researched online databases.
Background: The aim of the present study was to develop and validate a self-reported instrument exploring the perceived potential contribution and impact of a community-university partnership (CUP) on the local community's quality of life.
Methods: A 13-item questionnaire was developed and administered to a convenience sample of 65 residents of the municipality of Egaleo, a metropolitan area of Athens, Greece, where the main campus of the University of West Attica is located. The questionnaire was self-administered and filled in at two different time points by the same participants.
Cochrane Database Syst Rev
October 2024
This systematic review aimed to examine the efficacy and safety profile of amivantamab in patients with advanced or metastatic non-small cell lung cancer (NSCLC) and EGFR mutations. Three scientific databases, PubMed, Cochrane library and ClinicalTrials.gov were searched for relevant articles up until 30 June 2024.
View Article and Find Full Text PDF