Background: Medical education has observed numerous reforms in the last hundred years. While most of the reforms are applied to the undergraduate teaching, postgraduate education and training have lagged behind in keeping the pace. Anesthesiology curriculum has witnessed a few new methods inducted into practice like problem-based learning, flipped classroom etc. We introduced vertical integration with anatomy at our department and assessed its impact.
Methods: After a five-week schedule of integrated anatomy classes, a self-structured questionnaire was circulated amongst the 41 anesthesiology residents to know their perceptions and attitudes towards the classes. Their suggestions were also sought. The responses were analyzed with descriptive statistics (percentages).
Results: Thirty-six responses were received leading to a response rate of 87.8%. Fourteen residents (38.9%) believed that the integrated classes would be very helpful in their clinical practice, 20 (55.5%) residents believed them to be helpful while two (5.5%) residents believed that the classes would be little helpful in clinical practice. Hundred percent of the residents recommended the classes to be continued for the future batches. Half of the residents wanted the classes to be conducted twice in the three-year tenure (in the first and last semester) while 11 (30.5%) residents wanted the classes to be conducted every year. Seven (19.4%) residents thought that it's enough to conduct the classes once during the three-year tenure. Resident's suggested that they would like to have integrated classes with other departments like physiology, radiology, emergency medicine etc.
Conclusion: The integrated classes with anatomy were well perceived by the anesthesiology residents. Vertically integrated curriculum should be introduced in postgraduate training of various specialties for better education and hence, better patient care.
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http://dx.doi.org/10.1016/j.mjafi.2021.01.003 | DOI Listing |
J Med Case Rep
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Department of Surgery, Center for Endocrinology, Diabetes and Metabolism, Children's Hospital Los Angeles and Keck School of Medicine of USC, Los Angeles, CA, USA.
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January 2025
Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.
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January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
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ICF, Reston, VA, USA. Electronic address:
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Laoshan Laboratory, Qingdao 266237, China. Electronic address:
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