The transition to virtual learning formats during the COVID-19 pandemic necessitated substantial curricular adjustments to the University of Hawai'i John A. Burns School of Medicine. This study compares student satisfaction and academic performance between the pre-pandemic (up through March 25, 2020) and pandemic (after March 25, 2020) periods. Standard end of course surveys for first year (M1) and second year (M2) courses and exam scores were compared between the pre-pandemic and pandemic groups. The median exam scores for problem-based learning generally increased for M1 and M2 courses during the pandemic, whereas Anatomy scores showed variability with some declining and some remaining stable or inclining. End-course evaluations indicated a significant decrease in student-perceived effectiveness for PBL, Lecture and Anatomy during the initial pandemic period. However, survey ratings for the learning environment improved in later courses, suggesting adaptation over time. Notably, Anatomy exam scores and course ratings improved significantly later in the pandemic which may be attributed to the development of virtual resources and early introduction of in-person sessions. This study provides insight into the dynamic effects of the pandemic on medical education, enhancing understanding of student experiences and academic outcomes during this challenging time. This study underlines adaptations in the curriculum that were effective, highlighting the resilience of the curriculum and students in maintaining quality education during the pandemic.
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http://dx.doi.org/10.62547/IJCZ9506 | DOI Listing |
Hawaii J Health Soc Welf
January 2025
Office of Medical Education, John A. Burns School of Medicine, University of Hawai'i, Honolulu, HI (SFTF).
The transition to virtual learning formats during the COVID-19 pandemic necessitated substantial curricular adjustments to the University of Hawai'i John A. Burns School of Medicine. This study compares student satisfaction and academic performance between the pre-pandemic (up through March 25, 2020) and pandemic (after March 25, 2020) periods.
View Article and Find Full Text PDFBrain Dev
January 2025
Department of Neurology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China. Electronic address:
Background: Disease-modifying therapies can improve motor function in patients with spinal muscular atrophy (SMA), but efficacy varies between individuals. The aim was to evaluate the efficacy and safety of nusinersen treatment in children with SMA and to investigate prognostic factors.
Methods: Motor function, compound muscle action potential (CMAP), and other indicators were prospectively collected before and 14 months after nusinersen treatment.
Noise Health
January 2025
Department of Internal Medicine, Faculty of Medicine, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.
Background: The effect of background noise on auscultation accuracy for different lung sound classes under standardised conditions, especially at lower to medium levels, remains largely unexplored. This article aims to evaluate the impact of three levels of Gaussian white noise (GWN) on the ability to identify three classes of lung sounds.
Methods And Materials: A pre-post pilot study assessing the impact of GWN on a group of students' ability to identify lung sounds was conducted.
Breast Cancer Res Treat
January 2025
Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA, 02114, USA.
Purpose: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.
Methods: Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.
Alzheimers Dement
December 2024
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Background: In this pilot study, the diagnostic agreement for sleep biomarker-based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer's disease dementia (AD), Lewy body disease (LBD), mild cognitive impairment (MCI), and controls (CG) with a Mini-Mental State Exam scores ≥28.
Methods: Sleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc.) were used as inputs to a 4-class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, isolated REM sleep behavior disorder=15, and CG=58.
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