ABSTRACT
Coronary artery disease (CAD) is still a serious global health issue that has a substantial impact on death and illness rates. The goal of primary prevention strategies is to lower the risk of developing CAD. Nevertheless, current methods usually rely on simple risk assessment instruments that might overlook significant individual risk factors. This limitation highlights the need for innovative methods that can accurately assess cardiovascular risk and offer personalized preventive care. Recent advances in machine learning and artificial intelligence (AI) have opened up interesting new avenues for optimizing primary preventive efforts for CAD and improving risk prediction models. By leveraging large-scale databases and advanced computational techniques, AI has the potential to fundamentally alter how cardiovascular risk is evaluated and managed. This review looks at current randomized controlled studies and clinical trials that explore the application of AI and machine learning to improve primary preventive measures for CAD. The emphasis is on their ability to recognize and include a range of risk elements in sophisticated risk assessment models.
ABSTRACT
BACKGROUND: Effective learning strategies are crucial to the development of academic skills and information retention, especially in post secondary education where increasingly complex subjects are explored. Active recall-based strategies have been identified as particularly effective for long-term learning. This systematic review investigates the effectiveness of various active recall-based learning strategies for improving academic performance and self-efficacy in higher education students. METHODS: A systematic review of peer-reviewed articles was conducted with a priori criteria by searching PubMed, ScienceDirect, JSTOR, PsycInfo, and Web of Science databases. Search results were screened/extracted and reconciled by two independent authors with the use of a piloted screening tool. Included studies were assessed for quality and risk of bias using the GRADE Quality Assessment Tool for Quantitative Studies. Three overarching study strategies were extracted for further investigation including flashcards, practice testing or retrieval practice, and concept mapping. Within each category, three additional unique search strings were searched, screened, and extracted. A qualitative analysis of the studies was provided. RESULTS: Among the appraised articles, flashcards were found to be popular and correlated with higher GPA and test scores. Self-testing, retrieval practice, and concept mapping were also effective but under-utilized. Concept mapping was found to boost student confidence. CONCLUSION: Active recall strategies exhibit promise for effective learning and additional research in these developing field can support academic pursuits.
