Publications by authors named "Anup Kumar D Dhanvijay"

Introduction The COVID-19 pandemic significantly disrupted traditional educational methods, forcing medical institutes to adapt to online classes. Since online teaching was an untested approach in Indian medical education, student feedback was essential. This study compares synchronous online lectures with traditional classroom lectures from the students' perspective.

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Emotional intelligence (EI) has a positive correlation with the academic performance of medical students. However, why there is a positive correlation needs further exploration. We hypothesized that the capability of answering higher-order knowledge questions (HOQs) is higher in students with higher EI.

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Background Clinical case vignettes are a widely adopted pedagogical approach in medical education. The cases may be presented to students with a closed response option for objectivity. While solving clinical cases has demonstrated its effectiveness in enhancing medical students' clinical reasoning, there is an ongoing debate regarding the most effective approach: individual problem-solving or team-based problem-solving.

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Background Physical fitness is of utmost importance to athletes as it ensures better performance in competitive sports. Athletes who contracted COVID-19 frequently experienced persistent symptoms for weeks or months afterward. Due to the direct effects of COVID-19 infection on pulmonary, cardiovascular, and neurological systems, combined with the negative effects of isolation and inactivity, it has been observed that physical fitness decreases in individuals.

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Background Cardiovascular responses to exercise are essential indicators of cardiovascular health and fitness. Understanding how different types of exercise, such as lower-body and whole-body exercises, impact these responses is crucial for designing effective fitness programs and assessing cardiovascular function. Aim This study aimed to compare the cardiovascular response of young adults during lower-body exercise using a bicycle ergometer and whole-body exercise on a treadmill.

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Background Multiple choice questions (MCQs) are commonly used in medical exams for more objectivity in assessment. However, the quality of the questions should be optimum for a proper assessment of the students. A faculty development program (FDP) may improve the quality of MCQs.

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Background Large language models (LLMs), such as ChatGPT-3.5, Google Bard, and Microsoft Bing, have shown promising capabilities in various natural language processing (NLP) tasks. However, their performance and accuracy in solving domain-specific questions, particularly in the field of hematology, have not been extensively investigated.

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Background Large language models (LLMs) have emerged as powerful tools capable of processing and generating human-like text. These LLMs, such as ChatGPT (OpenAI Incorporated, Mission District, San Francisco, United States), Google Bard (Alphabet Inc., CA, US), and Microsoft Bing (Microsoft Corporation, WA, US), have been applied across various domains, demonstrating their potential to assist in solving complex tasks and improving information accessibility.

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