Background: Assessing in undergraduate medical education the educational effectiveness of a short computer-based session, integrating a lecturer's video with a standardized structure, for evidence based medicine (EBM) teaching, compared to a lecture-based teaching session of similar structure and duration.
Method: A concealed, randomized controlled trial of computer based session versus lecture of equal duration (40 minutes) and identical content in EBM and systematic reviews. The study was based at the Medical School, University of Birmingham, UK involving one hundred and seventynine year one medical students. The main outcome measures were change from pre to post-intervention score measured using a validated questionnaire assessing knowledge (primary outcome) and attitudes (secondary outcome).
Results: Participants' improvement in knowledge in the computer based group was equivalent to the lecture based group (gain in score: 0.8 [S.D = 3.2] versus 1.3 [S.D = 2.4]; p = 0.24). Attitudinal gains were similar in both groups.
Conclusion: Computer based teaching and typical lecture sessions have similar educational gains.
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http://dx.doi.org/10.1080/01421590701784349 | DOI Listing |
Addiction
March 2025
Division of Infectious Diseases and Global Public Health, University of California San Diego, San Diego, CA, USA.
Background And Aims: Better understanding the challenges faced by patients on medications for opioid use disorder (MOUD), including methadone and buprenorphine, is critical to increasing their use/retention. Social media platforms such as Reddit offer a space for patients to share their experiences with medications. We aimed to identify and characterize challenges faced by patients taking MOUD through analysis of discussions from the r/Methadone and r/suboxone subreddits.
View Article and Find Full Text PDFBrain Connect
March 2025
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
The brain's complex functionality emerges from network interactions that go beyond dyadic connections, with higher-order interactions significantly contributing to this complexity. Homotopic functional connectivity (HoFC) is a key neurophysiological characteristic of the human brain, reflecting synchronized activity between corresponding regions in the brain's hemispheres. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we evaluate dyadic and higher-order interactions of three functional connectivity (FC) parameterizations-bivariate correlation, partial correlation, and tangent space embedding-in their effectiveness at capturing HoFC through the inter-hemispheric analogy test.
View Article and Find Full Text PDFSmall
March 2025
Department of Materials Engineering and Organic Electronics Research Center, Ming Chi University of Technology, New Taipei City, 24301, Taiwan.
Metal halide perovskites are ideal candidates for indoor photovoltaics (IPVs) due to their tunable bandgaps, which allow the active layers to be optimized for artificial light sources. However, significant non-radiative carrier recombination under low-light conditions has limited the full potential of perovskite-based IPVs. To address this challenge, an integration of perylene diimide (PDI)-based sulfobetaines as cathode interlayers (CILs) is proposed and the impact of varying alkyl chain length (from 1,2-ethylene to 1,5-pentylene) between the cationic and the anionic moieties is examined.
View Article and Find Full Text PDFNetwork
March 2025
Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.
Non-Orthogonal Multiple Access (NOMA) is the successive multiple-access methodologies for modern communication devices. Energy Efficiency (EE) is suggested in the NOMA system. In dynamic network conditions, the consideration of NOMA shows high computational complexity that minimizes the EE to degrade the system performance.
View Article and Find Full Text PDFAppl Med Artif Intell (2024)
February 2025
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
Head motion is a major source of image artifacts in head computed tomography (CT), degrading the image quality and impacting diagnosis. Image-domain-based motion correction is practical for routine use since it doesn't rely on hard-to-obtain CT projection data. However, existing convolutional neural network (CNN)-based methods tend to over-smooth images, particularly in cases of moderate to severe 3D motion artifacts.
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