Relationship of emotional intelligence and capability of answering higher-order knowledge questions in physiology among first-year medical students.

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. Hence, we assessed the correlation between EI and the capability of medical students to answer HOQs in physiology. First-year undergraduate medical students (n = 124) from an Indian medical college were recruited as a convenient sample. EI was assessed by the Schutte Self-Report Emotional Intelligence Test (SSEIT), a 33-item self-administered validated questionnaire. A specially designed objective examination with 15 lower-order and 15 higher-order multiple-choice questions was conducted. The correlation between the examination score and the EI score was tested by Pearson's correlation coefficient. Data from 92 students (33 females and 59 males) with a mean age of 20.14 ± 1.87 yr were analyzed. Overall, students got a percentage of 53.37 ± 14.07 in the examination, with 24.46 ± 9.1 in HOQs and 28.91 ± 6.58 in lower-order knowledge questions (LOQs). They had a mean score of 109.58 ± 46.2 in SSEIT. The correlation coefficient of SSEIT score with total marks was r = 0.29 (P = 0.0037), with HOQs was r = 0.41 (P < 0.0001), and with LOQs was r = 0.14 (P = 0.19). Hence, there is a positive correlation between EI and the capability of medical students to answer HOQs in physiology. This study may be the foundation for further exploration of the capability of answering HOQs in other subjects.NEW & NOTEWORTHY This study assessed the correlation between emotional intelligence (EI) and the capability of medical students to answer higher-order knowledge questions (HOQs) in the specific context of physiology. The finding reveals one of the multifaceted dimensions of the relationship between EI and academic performance. This novel perspective opens the door to further investigations to explore the relationship in other subjects and other dimensions to understand why students with higher EI have higher academic performance.

Comparing the Efficacy of Individual Approaches and Team-Based Approaches in Solving Clinical Case Vignettes.

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. Objective To observe and compare the score obtained from individual clinical problem-solving approaches versus team-based clinical problem-solving approaches. Methods After obtaining consent, a total of 100 students were randomly selected for the study. The participants were divided into two groups: an individual approach group (IAG) (n=25) and a team-based approach group (TAG) comprising 25 groups of three students each. Both groups were presented with a set of 10 clinical problems, each requiring a closed-answer response of "yes", "no", or "don't know". The participants' responses were recorded and analyzed to evaluate their problem-solving efficacy. Results A total of 25 responses were obtained from 25 students from the IAG group and 25 responses from 25 groups from the TAG group. There was no difference between the score in IAG (7.44±1.12) and TAG (7.52 1.66) p-value=0.58. There was no difference between individual scores in 10 questions between IAG and TAG groups. Conclusion The study found no significant score differences between individual and team-based clinical case-solving groups. Hence, for the objective type of case-solving pattern used in this study, a team-based approach may not be necessary. Further research is needed to explore factors for such findings in future studies.

Comparison of Cardiovascular Response to Lower Body and Whole Body Exercise Among Sedentary Young Adults.

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. Methods Thirty-two healthy young adults participated in this study. Each participant completed two exercise sessions on separate days: lower-body exercise on a bicycle ergometer with a fixed cadence of 60 rpm with a breaking resistance of 1.75 kg and whole-body exercise on a treadmill with a speed of 1.7 mph and a 10% grade. Heart rate (HR), systolic blood pressure (BP), and diastolic BP were measured at rest and immediately after a three-minute exercise. Data were analyzed using paired t-tests to compare the cardiovascular responses between the two exercise modalities. Results A total of 17 male and 15 female young adults with a mean age of 20.87±1.43 years participated in the study. The male and female participants had similar ages (p =0.56) and body mass indexes (p = 0.1). There was a higher HR (129.16±2.67 versus 150.87±3.23, p<0.0001) and systolic BP (127.29±2.34 versus 144.9±4.16, p<0.0001) and lower diastolic BP (68.97±2.41 versus 62.97±2.31, p<0.0001) in whole body exercise on treadmill compared to lower body exercise in bicycle ergometer. The effect size was large enough as Cohen's d was 7.33, 5.13, and 2.54 for HR, systolic BP, and diastolic BP, respectively. Conclusion In sedentary young adults, treadmill exercise led to higher HR, systolic BP, and lower diastolic BP than bicycle ergometer exercise. Increased muscle recruitment might result in higher energy expenditure, increasing the HR and systolic BP to deliver oxygen and nutrients to the working muscles. Further research is needed to understand the mechanisms and long-term implications for precise exercise recommendations and better cardiovascular health management.

Impact of COVID-19 on Physical Fitness in Central Indian Athletes Aged 20-30 Years: A Cross-Sectional Pilot Study.

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. This study aimed to evaluate the physical fitness of young athletes in the age group of 20 to 30 years after mild-to-moderate COVID-19 infection and compare them with unaffected athletes of the same age group. Methodology A field-based, cross-sectional, comparative study was conducted from July 2022 to August 2022 in Nagpur, India. Physical fitness levels of 50 young athletes in the age group of 20-30 years who never got infected with COVID-19 were compared to 50 athletes with a recent history of mild-to-moderate COVID-19 infection using the Harvard step test, breath-holding test, and peak expiratory flow rate measurement. Participants were included based on COVID-19 diagnosis using standard procedures and confirmation of recovery through negative reverse transcriptase polymerase chain reaction tests. Results Overall physical fitness of athletes who suffered from mild-to-moderate COVID-19 infection was significantly less than those who were not infected. Compared to their non-COVID-19 counterparts, the COVID-19-recovered athletes showed reduced physical fitness index (p < 0.0001 for males and p = 0.0003 for females), reduced peak expiratory flow rate (p < 0.0001 for males and p < 0.0001 for females), and reduced breath-holding time (p < 0.0001 for males and p < 0.0001 for females). Conclusions COVID-19 had a significant impact on various components of physical fitness which may potentially affect the athletic performance and overall well-being of young athletes.

Large Language Models in Hematology Case Solving: A Comparative Study of ChatGPT-3.5, Google Bard, and Microsoft Bing.

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. Objective This study aimed to explore the capability of LLMs, namely, ChatGPT-3.5, Google Bard, and Microsoft Bing (Precise), in solving hematology-related cases and comparing their performance. Methods This was a cross-sectional study conducted in the Department of Physiology and Pathology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India. We curated a set of 50 cases on hematology covering a range of topics and complexities. The dataset included queries related to blood disorders, hematologic malignancies, laboratory test parameters, calculations, and treatment options. Each case and related question was prepared with a set of correct answers to compare with. We utilized ChatGPT-3.5, Google Bard Experiment, and Microsoft Bing (Precise) for question-answering tasks. The answers were checked by two physiologists and one pathologist. They rated the answers on a rating scale from one to five. The average score of the three models was compared by Friedman's test with Dunn's post-hoc test. The performance of the LLMs was compared with a median of 2.5 by a one-sample median test as the curriculum from which the questions were curated has a 50% pass grade. Results The scores among the three LLMs were significantly different (p-value < 0.0001) with the highest score by ChatGPT (3.15±1.19), followed by Bard (2.23±1.17) and Bing (1.98±1.01). The score of ChatGPT was significantly higher than 50% (p-value = 0.0004), Bard's score was close to 50% (p-value = 0.38), and Bing's score was significantly lower than the pass score (p-value = 0.0015). Conclusion The LLMs reveal significant differences in solving case vignettes in hematology. ChatGPT exhibited the highest score, followed by Google Bard and Microsoft Bing. The observed performance trends suggest that ChatGPT holds promising potential in the medical domain. However, none of the models was capable of answering all questions accurately. Further research and optimization of language models can offer valuable contributions to healthcare and medical education applications.

Performance of Large Language Models (ChatGPT, Bing Search, and Google Bard) in Solving Case Vignettes in Physiology.

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. However, their application in solving case vignettes in physiology has not been explored. This study aimed to assess the performance of three LLMs, namely, ChatGPT (3.5; free research version), Google Bard (Experiment), and Microsoft Bing (precise), in answering cases vignettes in Physiology. Methods This cross-sectional study was conducted in July 2023. A total of 77 case vignettes in physiology were prepared by two physiologists and were validated by two other content experts. These cases were presented to each LLM, and their responses were collected. Two physiologists independently rated the answers provided by the LLMs based on their accuracy. The ratings were measured on a scale from 0 to 4 according to the structure of the observed learning outcome (pre-structural = 0, uni-structural = 1, multi-structural = 2, relational = 3, extended-abstract). The scores among the LLMs were compared by Friedman's test and inter-observer agreement was checked by the intraclass correlation coefficient (ICC). Results The overall scores for ChatGPT, Bing, and Bard in the study, with a total of 77 cases, were found to be 3.19±0.3, 2.15±0.6, and 2.91±0.5, respectively, p<0.0001. Hence, ChatGPT 3.5 (free version) obtained the highest score, Bing (Precise) had the lowest score, and Bard (Experiment) fell in between the two in terms of performance. The average ICC values for ChatGPT, Bing, and Bard were 0.858 (95% CI: 0.777 to 0.91, p<0.0001), 0.975 (95% CI: 0.961 to 0.984, p<0.0001), and 0.964 (95% CI: 0.944 to 0.977, p<0.0001), respectively. Conclusion ChatGPT outperformed Bard and Bing in answering case vignettes in physiology. Hence, students and teachers may think about choosing LLMs for their educational purposes accordingly for case-based learning in physiology. Further exploration of their capabilities is needed for adopting those in medical education and support for clinical decision-making.

The Effect of a One-Day Workshop on the Quality of Framing Multiple Choice Questions in Physiology in a Medical College in India.

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. The effect of a one-day workshop on framing MCQ as a part of a FDP has not been explored in our institution. Aim This study aimed to evaluate the quality of MCQ in the subject of physiology before and after a one-day workshop on framing MCQ as a part of a FDP. Methods This was a retrospective study conducted in the Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India. A one-day workshop on framing MCQ as a part of a FDP was conducted in March 2022. We took 100 MCQs and responses from the students from examinations conducted before the workshop and 100 MCQs and responses from the students after the workshop. In pre-workshop and post-workshop, the same five faculties framed the questions. Post-validation item analysis including difficulty index (DIFI), discrimination index (DI), distractor effectiveness (DE), and Kuder-Richardson Formula 20 (KR-20) for internal consistency was calculated. Results Pre-workshop and post-workshop quality of the MCQ remain equal in terms of DIFI (chi-square {3} = 2.42, P = 0.29), DI (chi-square {3} = 2.44, P = 0.49), and DE (chi-square {3} = 4.97, P = 0.17). The KR-20 in pre-workshop and post-workshop was 0.65 and 0.87, respectively. Both had acceptable internal consistency. Conclusion The one-day workshop on framing MCQs as a part of a FDP did not have a significant impact on the quality of the MCQs as measured by the three indices of item quality but did improve the internal consistency of the MCQs. Further educational programs and research are required to find out what measures can improve the quality of MCQs.