Results 251 to 260 of about 1,777,903 (330)

Educational data mining in medical education: A five-level approach. [PDF]

J Educ Health Promot
Khoshgoftar Z, Babaee M, Rouzbahani AK, Kalantarion M.   +3 more
europepmc   +1 more source

Comparing the impact of online and in-person active learning in preclinical medical education. [PDF]

BMC Med Educ
Malta K, Glickman C, Hunter K, McBride A.   +3 more
europepmc   +1 more source

Medical Education and Television

, 1951
V. F. Bazilauskas, Bernard V. Dryer, David S. Ruhe   +2 more
openalex   +1 more source

Accuracy of LLMs in medical education: evidence from a concordance test with medical teacher. [PDF]

BMC Med Educ
Mishra V, Lurie Y, Mark S.
europepmc   +1 more source

Physical Therapy in Medical Education

, 1940
John S. Coulter
openalex   +1 more source

Connecting for Success: The Role of Networking in Medical Education. [PDF]

Cureus
Wani P.
europepmc   +1 more source

Improving organ dose sparing in left‐sided breast cancer with yaw‐limited volumetric modulated arc therapy: A dosimetric comparison to conventional and intensity modulated radiation therapy approaches

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background To assess the dose‐sparing capabilities of a yaw‐limited volumetric modulated arc therapy (YL_VMAT) beam setup for adjacent organs at risk (OAR) in comparison with 3D‐conventional radiation therapy (3D‐CRT), intensity‐modulated radiation therapy (IMRT) and conventional VMAT for radiation therapy in left‐sided breast cancer patients.
Gerhard Pollul, Sascha Grossmann, Heiko Karle, Tilman Bostel, Heinz Schmidberger   +4 more
wiley   +1 more source

The Importance of Basic Research Knowledge in Undergraduate Medical Education. [PDF]

Kans J Med
Ofei-Dodoo S.
europepmc   +1 more source

A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Studies on deep learning dose prediction increasingly focus on 3D models with multiple input channels and data augmentation, which increases the training time and thus also the environmental burden and hampers the ease of re‐training. Here we compare 2D and 3D U‐Net models with clinical accepted plans to evaluate the appropriateness of
Rosalie Klarenberg, Nienke L. M. Bakx, Coen W. Hurkmans   +2 more
wiley   +1 more source

A New Journal in Medical Education: The Canadian Medical Education Journal

Canadian Medical Education Journal, 2010
Claudio Violato, Tyrone Donnon
doaj