It has been shown that surgical residents who took few or no in-house calls during medical school felt less prepared for the residency. In this study, our objective was to assess the impact of in-house calls carried out by medical students on their perceptions of medical training, including the influence on specialty choice. The students were asked to complete an anonymized questionnaire at the first and last day of their general surgery clerkship. Students were asked regarding importance for medical training and education, preparation for the internship, learning opportunities, skills acquisition; negative effects, including fatigue, negative effect over medical training, personal life, and physical and mental health derangements; and the student's perception of the residents' in-house calls and parameters affecting specialty selection: difficulty of the residency, prestige, and future career opportunities. A total of 42 medical students responded to 84 questionnaires. There was a significant difference in the importance of calls among male students before the beginning of the clerkships compared with the end of the clerkship (4.53 versus 4.21, = .034). At the end of general surgery clerkship, students indicated that the calls less impaired studying during the clerkship (2.5 versus 2.21, < .05) compared with the beginning of the clerkship. Female students ranked the calls as less demanding at the end of the clerkship (2.53 versus 2.12, < .05). The impact of the residency difficulty on the selection of their future specialty was rated higher by the students at the end of the clerkship compared with their expectations at the beginning (3.13 versus 2.85, = .033). In conclusion, our study demonstrates that in-house calls performed by medical students during their general surgery clerkships have a significant influence on their perceptions of medical training and choice of specialty. The study also highlights the importance of gender differences in the students' perception of the importance and impact of calls on their well-being.
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http://dx.doi.org/10.1089/lap.2023.0484 | DOI Listing |
Front Oncol
October 2024
AI Lab, Ardigen SA, Cracow, Poland.
Purpose: Developing innovative precision and personalized cancer therapeutics is essential to enhance cancer survivability, particularly for prevalent cancer types such as colorectal cancer. This study aims to demonstrate various approaches for discovering new targets for precision therapies using artificial intelligence (AI) on a Polish cohort of colorectal cancer patients.
Methods: We analyzed 71 patients with histopathologically confirmed advanced resectional colorectal adenocarcinoma.
Brief Bioinform
July 2024
Department of Quantitative and Computational Biology, Dana and David Dornsife College of Letters, Arts and Sciences University of Southern California, 3540 S Figueroa St, Los Angeles, California 90089, United States.
Structural variation (SV) refers to insertions, deletions, inversions, and duplications in human genomes. SVs are present in approximately 1.5% of the human genome.
View Article and Find Full Text PDFJ Mol Diagn
November 2024
Lab Operations, Foundation Medicine GmbH, Penzberg, Germany. Electronic address:
Stud Health Technol Inform
August 2024
Institute for Digital Medicine, University Hospital Augsburg, Germany.
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue tiles.
View Article and Find Full Text PDFMol Diagn Ther
November 2024
Department of Laboratory Medicine and Pathology, University of Minnesota, 100 Church St SE, Minneapolis, MN, 55455, USA.
Background: Next-generation sequencing is widely used for comprehensive molecular profiling for many cancers including lung cancer. However, the complex workflows and long turnaround times limit its access and utility. ChromaCode's High Definition PCR Non-Small Cell Lung Cancer Panel (HDPCR™ NSCLC Panel) is a low-cost, rapid turnaround, digital polymerase chain reaction assay that is designed to detect variants in nine NSCLC genes listed in National Comprehensive Cancer Network guidelines.
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