Purpose: To design, develop, and evaluate an interactive simulation-based learning tool for treatment plan evaluation for radiation oncology and medical physics residents to address gaps in learning.
Methods And Materials: We first conducted a needs assessment for optimal learning tool design and case selection. Next, we generated a curated database of cases with clinically unacceptable treatment plans accessible through an in-house developed interactive web-based digital imaging and communications in medicine-radiation therapy viewer. We then developed an interactive user module that allows case selection, learner participation, and immediate feedback, including the final clinically acceptable plan. We pilot tested this case bank learning tool with current radiation oncology and medical physics residents within our institution. Afterward, residents completed an evaluation of tool design, content, and perceived impact on learning and provided suggestions for improvement.
Results: We generated 70 cases and learning modules for the case bank, encompassing various clinical sites, levels of difficulty, and classified errors. Residents positively endorsed the learning tool, including design, content, and perceived impact on learning. The learning tool's interactivity was perceived to provide increased educational value compared with other current learning methods.
Conclusions: We created a high-fidelity simulation platform for treatment plan evaluation linked to a curated case bank. Evaluation of the pilot deployment demonstrated a benefit for resident learning and competency attainment. Future directions include external validation and expansion.
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http://dx.doi.org/10.1016/j.ijrobp.2020.03.018 | DOI Listing |
Sci Rep
December 2024
Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, 250014, Shandong, People's Republic of China.
This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.
View Article and Find Full Text PDFCurr Biol
December 2024
Department of Neurobiology, University of Utah, 20 S 2030 E, BPRB 490D, Salt Lake City, UT 84112, USA. Electronic address:
Integrative studies of diverse neuronal networks that govern social behavior are hindered by a lack of methods to record neural activity comprehensively across the entire brain. The recent development of the miniature fish Danionella cerebrum as a model organism offers one potential solution, as the small size and optical transparency of these animals make it possible to visualize circuit activity throughout the nervous system. Here, we establish the feasibility of using Danionella as a model for social behavior and socially reinforced learning by showing that adult fish exhibit strong affiliative tendencies and that social interactions can serve as the reinforcer in an appetitive conditioning paradigm.
View Article and Find Full Text PDFSurgery
December 2024
Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. Electronic address:
Background: Duodenal stump leakage is one of the most critical complications following gastrectomy surgery, with a high mortality rate. The present study aimed to establish a predictive model based on machine learning for forecasting the occurrence of duodenal stump leakage in patients who underwent laparoscopic gastrectomy for gastric cancer.
Materials And Methods: The present study included the data of 4,070 patients with gastric adenocarcinoma who received laparoscopic gastrectomy.
Clin Transl Med
January 2025
Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Precision medicine in less-defined subtype diffuse large B-cell lymphoma (DLBCL) remains a challenge due to the heterogeneous nature of the disease. Programmed cell death (PCD) pathways are crucial in the advancement of lymphoma and serve as significant prognostic markers for individuals afflicted with lymphoid cancers. To identify robust prognostic biomarkers that can guide personalized management for less-defined subtype DLBCL patients, we integrated multi-omics data derived from 339 standard R-CHOP-treated patients diagnosed with less-defined subtype DLBCL from three independent cohorts.
View Article and Find Full Text PDFBMC Med Educ
December 2024
Department of Gastroenterology, Wenzhou Third Clinical Institute Affiliated to Wenzhou Medical University, The Third Affiliated Hospital of Shanghai University, Wenzhou People's Hospital, Wenzhou Maternal and Child Health Care Hospital, No. 57 Cang Hou Street, Wenzhou, Zhejiang, 325000, China.
Objective: This study aims to explore the effect of an innovative teaching model incorporating ChatGPT on medical students' learning outcomes, compliance with learning activities, and overall satisfaction with the learning process.
Methods: A cohort of 64 students participating in general surgery clerkships at Wenzhou People's Hospital during the 2022-2023 academic year were randomly assigned into 4 groups, each comprising 16 students. Two of these groups were designated as the study group, where ChatGPT was employed as a supplementary educational tool.
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