Perceptions of Medical Students on Research Curriculum: A Cross-sectional Study

Introduction: Medical colleges promote research by incorporating it into the curriculum, which enables students to acknowledge it as a career prospect. The aim of the study was to assess the perceptions of medical students on research curriculum. Methods: This cross-sectional study was conducted among 544 medical students (interns and post-graduates) at AIIMS Rishikesh in 2020. Data was collected thorough online self-administered questionnaire. A comparison between groups was made using the Mann-Whitney test or chi-square test p < 0.05 was considered statistically significant. Results: Out of 544 participants, 218 (40.1%) responded with complete data. The total median score for the self-perceived ability of study participants regarding performing the research tasks differed significantly between interns and post-graduates [29.5 (24.0–34.2) vs 33 (25.2–39.7), p = 0.03]. They suggested that mandatory research projects, workshops, and training should be included in the curriculum. Conclusion: Feedback from medical students regarding the need for guided research projects, hands-on training, and inclusion of research methodology as a course in UG curriculum and provision of support in the form of incentives, academic credits, and motivation are well noted and guide the resource faculties to modify their teaching and student support programs

Prevalence of asymptomatic bacteriuria (ASB) in pregnant women in India: A systematic review and meta-analysis

Background: Asymptomatic bacteriuria is prevalent during pregnancy. If it goes undetected, it can lead to urinary tract infection with severe maternal and neonatal complications. Until date, India does not have any guidelines to test for ASB during pregnancy. Objective: To estimate the pooled prevalence of asymptomatic bacteriuria in pregnant women at national level in India. Material and Methods: We searched Medline, Embase, Web of Science, and Google Scholar using search strategy with keyword. Two authors independently assessed the eligibility of study. The checklist of the JBI was used for evaluating the quality of reporting. The extracted data were analyzed, and the results were reported using a random-effects model with 95% confidence interval (CI). Subgroup analysis was conducted for zones of India, parity and trimester. Publication bias is reported as funnel plot. Result: Pooled prevalence of asymptomatic bacteriuria among pregnant women in India is 13.5% [CI 11.1; 15.8]. Subgroup analysis based on the various geographic zones of the country the pooled prevalence ranged from 9.2% in central zone to 14.8% in south zone. Distribution of prevalence of ASB as per parity was approximately identical. The prevalence of ASB was found to be high in third (21.8%). Conclusion: The prevalence of ASB is found to be high among Indian pregnant women, especially in third trimester. It is therefore recommended for guideline to screen and treat every pregnant woman for ASB to prevent further complications

Buckling Analysis of Roll Cage Using Finite Element Analysis

Abstract — The present paper is based on frame buckling analysis of ATV roll cage through finite element procedures.This analysis was performed using CATIA software. The detailed three dimensional model geometry of roll cage is worked out. The meshing of the part blower is done and the details of the finite element mesh are calculated. The roll cage is subjected to the load environment. The actual load conditions are worked out and the boundary conditions are given.This analysis comprises of material selection, structure computation, restraint computation, load computation, stiffness computation, singularity computation, constraint computation, factorized computation, direct method computation, geometric stiffness computation and buckling computation and at last global sensors

Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study

Peer reviewed: TrueFunder: National Institute for Health and Care Research; FundRef: http://dx.doi.org/10.13039/501100000272Objective: To determine whether an artificial intelligence candidate could pass the rapid (radiographic) reporting component of the Fellowship of the Royal College of Radiologists (FRCR) examination. Design: Prospective multi-reader diagnostic accuracy study. Setting: United Kingdom. Participants: One artificial intelligence candidate (Smarturgences, Milvue) and 26 radiologists who had passed the FRCR examination in the preceding 12 months. Main outcome measures: Accuracy and pass rate of the artificial intelligence compared with radiologists across 10 mock FRCR rapid reporting examinations (each examination containing 30 radiographs, requiring 90% accuracy rate to pass). Results: When non-interpretable images were excluded from the analysis, the artificial intelligence candidate achieved an average overall accuracy of 79.5% (95% confidence interval 74.1% to 84.3%) and passed two of 10 mock FRCR examinations. The average radiologist achieved an average accuracy of 84.8% (76.1-91.9%) and passed four of 10 mock examinations. The sensitivity for the artificial intelligence was 83.6% (95% confidence interval 76.2% to 89.4%) and the specificity was 75.2% (66.7% to 82.5%), compared with summary estimates across all radiologists of 84.1% (81.0% to 87.0%) and 87.3% (85.0% to 89.3%). Across 148/300 radiographs that were correctly interpreted by >90% of radiologists, the artificial intelligence candidate was incorrect in 14/148 (9%). In 20/300 radiographs that most (>50%) radiologists interpreted incorrectly, the artificial intelligence candidate was correct in 10/20 (50%). Most imaging pitfalls related to interpretation of musculoskeletal rather than chest radiographs. Conclusions: When special dispensation for the artificial intelligence candidate was provided (that is, exclusion of non-interpretable images), the artificial intelligence candidate was able to pass two of 10 mock examinations. Potential exists for the artificial intelligence candidate to improve its radiographic interpretation skills by focusing on musculoskeletal cases and learning to interpret radiographs of the axial skeleton and abdomen that are currently considered “non-interpretable.