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.

Student perceptions of Twitter poll-based formative assessment in a rural medical college in India.

Formative assessment is vital for student learning and engagement. Social media platforms like Twitter have gained popularity in medical education, but little research has explored student perceptions of formative assessment through Twitter. This study aimed to observe participation rates in Twitter poll-based formative assessment and survey students' perceptions of it in a rural medical college in India. Fifteen formative assessment sessions were conducted using a Twitter poll after physiology lectures, with five multiple-choice questions per session. The number of participants in each poll was recorded. A questionnaire collected student perceptions after the sessions. Across 15 Twitter poll sessions, we received an average of 12.65 ± 5.53 (median 12) responses. Most students (63%) did not participate, with only 4% participating in over 10 sessions. A total of 107 students participated in the survey, resulting in an 85.6% response rate. Students found social media-based formative assessment interesting and effective for increasing attention in class. However, they preferred alternative platforms like Telegram and Instagram for formative assessment. In conclusion, most students in a rural medical college in India do not participate in Twitter poll-based formative assessment. Nevertheless, students found it interesting and effective, highlighting the importance of considering students' platform preferences for implementing social media-based formative assessment.NEW & NOTEWORTHY The study presents the participation of medical students in Twitter poll-based formative assessment in a rural medical college in India. Despite low participation rates, students found social media-based formative assessment highly effective in increasing their attention during class. The study also reveals student preferences for alternative platforms like Telegram and Instagram. These insights contribute significantly to understanding the impact and relevance of social media-based formative assessment in diverse educational contexts.

A Brief Review on Good Clinical Practice and its Training Methods.

The field of clinical research continues to advance, and for ethical conduct of research, researchers need to have a strong foundation in good clinical practice (GCP). GCP guidelines are internationally recognized standards that govern the conduct of clinical trials, ensuring the protection of human subjects and the generation of reliable data. However, novice researchers or postgraduate medical students who would delve into research activities may face difficulty getting avenues for GCP training. Hence, in this brief review article, we discuss the significance of GCP in the field of clinical research with a glimpse of its history, development, and key principles. We provide a list of online courses, benefits, and disadvantages of those courses, and a list of organizations that conduct GCP workshops or continued medical education programs in India.

Comparison of corrected QT (QTc) interval of electrocardiogram calculated from various formulas.

QT interval in an electrocardiogram (ECG) is interpreted after correction (QTc) by various formulas. This study aimed to compare the QTcs calculated by nine formulas. Sinus rhythm ECG reports of 1140 anonymous subjects showed uncorrected QT interval of 388.49 ± 42.74 ms. The QTc calculated by Bazett (443.96 ± 57.58 ms), Fridericia (424.37 ± 50.1 ms), Dmitrienko (433.59 ± 53.37 ms), Framingham (422.59 ± 45.55 ms), Schlamowitz (433.89 ± 48.05 ms), Hodges (421.6 ± 46.4 ms), Ashman (434.33 ± 54.05 ms), Rautaharju (427.75 ± 47.4 ms), and Sarma (429.22 ± 48.67 ms) showed a significant difference F (8, 10251) = 22.78 p < 0.0001. Hence, ECG should contain the formula for proper reporting and ease of interpretation by clinicians.

Naturopathic Interventions for Reduction of Perceived Pain in Patients Suffering from Arthritis: A Systematic Review and Meta-Analysis.

Arthritis affects millions of lives with its pervasive effects on physical health and quality of life. Addressing the complexities of managing symptoms such as swelling, inflammation, and pain requires prolonged treatment. Naturopathy is a treatment method that enhances the body's innate ability to restore optimal health through a holistic approach including natural products and lifestyle modifications. This systematic review addresses the intersection of naturopathy and arthritis treatment to provide current evidence about its potential benefits. Four databases (PubMed, AYUSH Research Portal, Web of Science, and Google Scholar) were searched with the keywords "Naturopathy" AND "Arthritis". Randomized, non-randomized, and cross-over studies in English were included. Studies reporting perceived pain using a visual analogue scale (VAS) were selected for meta-analysis. A total of 15 studies were included in the systematic review. The studies were from Denmark, Egypt, France, Hungary, Israel, Italy, Spain, and Turkey, and the study periods ranged from 1992 to 2017. They suggested that naturopathic treatment modalities like exercise, mud compress, sand bath, or hydrotherapy may be used in addition to conventional modes of treatment for added benefit. There was a diversity of naturopathic treatment modalities and outcome evaluation methods. Most studies used mud compress or mud baths with reported improvement of symptoms. The meta-analysis of 10 studies (11 sets of data) showed a significant improvement in pain measured by VAS. The studies included in the review have a high level of heterogenicity. There is a need for more studies and uniform assessment methods with standardization of interventions for robust evidence. More clinical trials from countries where naturopathy is approved treatment modalities are needed.

Efficacy of ChatGPT in solving attitude, ethics, and communication case scenario used for competency-based medical education in India: A case study.

BACKGROUND: Competency-based medical education (CBME) is a method of medical training that focuses on developing learners' competencies rather than simply assessing their knowledge and skills. Attitude, ethics, and communication (AETCOM) are important components of CBME, and the use of artificial intelligence (AI) tools such as ChatGPT for CBME has not been studied. Hence, we aimed to assess the capability of ChatGPT in solving AETCOM case scenarios used for CBME in India. MATERIALS AND METHODS: A total of 11 case scenarios were developed based on the AETCOM competencies. The scenarios were presented to ChatGPT, and the responses generated by ChatGPT were evaluated by three independent experts by awarding score ranging from 0 to 5. The scores were compared with a predefined score of 2.5 (50% accuracy) and 4 (80% accuracy) of a one-sample median test. Scores among the three raters were compared by the Kruskal-Wallis H test. The inter-rater reliability of the evaluations was assessed using the intraclass correlation coefficient (ICC). RESULTS: The mean score of solution provided by ChatGPT was 3.88 ± 0.47 (out of 5), indicating an accuracy of approximately 78%. The responses evaluated by three raters were similar (Kruskal-Wallis H P value 0.51), and the ICC value was 0.796, which indicates a relatively high level of agreement among the raters. CONCLUSION: ChatGPT shows moderate capability in solving AETCOM case scenarios used for CBME in India. The inter-rater reliability of the evaluations suggests that ChatGPT's responses were consistent and reliable. Further studies are needed to explore the potential of ChatGPT and other AI tools in CBME and to determine the optimal use of these tools in medical education.

Social Media and Urticaria - A Data Audit of Facebook®, LinkedIn®, and Twitter® Posts.

Introduction: Urticaria is a common debilitating dermatological disorder impairing a patient's quality of life. Such patients are increasingly using socialmedia to manage their health and interact with peers, particularly during the ongoing COVID-19 pandemic. Objectives: To explore and analyse the quality of urticaria related social-media information available to patients. Materials and Methods: An in-depth data audit of the three most commonly used social networks viz. Facebook®, LinkedIn®, and Twitter® were done on a single day, as posts may change or lose relevance over time. The word "urticaria" was searched on three social media, and the first 100 posts in each were further analysed. The post-creator was either categorised as "individual" or "group", and non-English posts were excluded. All types of posts have been analysed, including text, images, video, and website links. We also collected the comments/replies, share/re-tweet, and likes on the posts. Results: Among the total 300 social-media posts, the highest number of "individual" posts was on LinkedIn® followed by Twitter® and Facebook® (χ2 = 82.86, P < 0.0001). Regarding thematic content, most Facebook® posts discussed disease symptoms, followed by the promotion of journal or blog posts, and discussion about causative and triggering agents. LinkedIn® was primarily used for the promotion of journal articles or blog posts, followed by educational webinars and urticaria treatment stories. Twitter® users mostly interacted with peers about their urticaria symptoms and perceived etiologic and triggering factors. Regarding the type of post, images were maximally shared on Facebook®, while video/video links and web links were highest on LinkedIn® (χ2 = 21.59, P < 0.0001). Conclusion: The overall quality of urticaria related information on these 3 social media platforms is satisfactory for patients. Dermatologists may consider utilising social media to further educate such patients and improve the overall treatment outcome. The use of such networking channels will continue to grow, as communication remains crucial for urticaria management.

Accuracy of a Non-Invasive Home Glucose Monitor for Measurement of Blood Glucose.

Introduction: Patients with diabetes mellitus monitor their blood glucose at home with monitors that require a drop of blood or use a continuous glucose monitoring device that implants a small needle in the body. However, both cause discomfort to the patients which may inhibit them for regular blood glucose checks. Photoplethysmogram (PPG) sensing technology is an approach for non-invasive blood glucose measurement and PPG sensors can be used to predict hypoglycaemic episodes. InChcek is a PPG-based non-invasive glucose monitor. However, its accuracy has not been checked yet. Hence, this study aimed to evaluate the accuracy of InCheck, a non-invasive glucose monitor for the estimation of blood glucose. Methods: In a tertiary care hospital, patients who came for blood glucose estimation were tested for blood glucose non-invasively on the InCheck device and then by the laboratory method (glucose oxidase-peroxidase). These two readings were compared. We used International Organization for Standardization (ISO) 15197:2013 (95% of values should be within ± 15 mg/dL of reference reading if reference glucose <100 mg/dL or within ± 15% of reference reading if reference glucose ≥100 mg/dL and 99% of the values should be within zones A and B in consensus error grid), and Surveillance Error Grid for analyzing the accuracy. Results: A total of 1223 samples were analyzed. There was a significant difference between the reference method glucose level (135 [Q1-Q3: 97 - 179] mg/dL) and monitor-measured glucose level (188.33 [Q1-Q3: 167.33-209.33] mg/dL) (P < 0.0001). A total of 18.5% of readings were following ISO 15197:2013 criteria and 67.25% of coordinates were within zone A and zone B of the consensus error grid. In the surveillance error grid analysis, about 29.4% of values were in the no-risk zone, 51.8% in slight risk, 18.6% in moderate risk, and 0.2% were in the severe risk zone. Conclusion: The accuracy of the InCheck device for the estimation of blood glucose by PPG signal is not following the recommended guidelines. Hence, further research is necessary for programming or redesigning the hardware and software for a better result from this optical sensor-based non-invasive home glucose monitor.

Predictors of Recovery in Facial Nerve Palsy: Insights From an Observational Study in Odisha, India.

Background Facial nerve paralysis, leading to the loss of facial expression, poses significant discomfort to patients. While most individuals exhibit a favorable response to treatment, a subset experiences enduring facial deformities without clearly defined etiology. This study aimed to identify prognostic factors influencing outcomes and quality of life in facial nerve palsy patients, contributing to enhanced clinical management. Methods A prospective observational study was conducted in the Otorhinolaryngology Department of Maharaja Krishna Chandra Gajapati Medical College and Hospital, a tertiary care hospital. We included patients presenting with any clinical variety of facial nerve palsy, irrespective of age and gender. Only moribund and noncompliant cases were excluded. Patients underwent clinical assessment using the House-Brackmann (HB) grading at presentation and were subsequently monitored at three weeks, three months, and six months post-onset to assess recovery. Results Out of 66 patients, 18 (27.27%) fully recovered at three weeks, 50 (75.76%) recovered at three months, and 54 (81.82%) at six-month follow-up. Incomplete recovery was observed in 13 (19.69%) patients. Factors associated with favorable outcomes included younger age of onset (p = 0.003), lower baseline HB grade (IV or less) (p = 0.001), Electroneurography Degeneration Index (ENoG DI) of <70% (p < 0.0001), early initiation of treatment (within five days of onset) (p = 0.0003), and absence of comorbid conditions (p = 0.03). Gender and affected side (left or right) did not influence the outcome. Conclusion In summary, age, associated comorbid conditions, baseline HB grade, and extent of facial nerve degeneration are crucial predictors of outcomes in facial nerve palsy. This knowledge can guide clinicians in optimizing treatment strategies for improved patient care.

A Cross-Sectional Study on Motor and Sensory Nerve Functions in Women Newly Diagnosed and Untreated for Hypothyroidism in a Tribal Area of Odisha, India.

Introduction: Altered levels of thyroid hormones can impact various body systems, including the nervous system. Hypothyroidism may disrupt nerve conduction due to pathophysiological changes associated with hormone deficiency. The tribal population, characterized by distinct lifestyles and dietary habits, may experience unique influences on their growth and development. Aim: This study aimed to compare nerve conduction in newly diagnosed and untreated tribal women affected by hypothyroidism with euthyroid tribal women. Methods:A cross-sectional study was conducted in southern Odisha, India, spanning from April 2020 to January 2021. Forty-five newly diagnosed hypothyroid tribal women were enlisted from the outpatient department of general medicine as the case group. Additionally, 45 age-matched apparently healthy euthyroid tribal women were included as the control group. The subjects' height and weight were measured by an expert clinician. Nerve conduction (motor and sensory) study on both extremities (left and right side) were conducted for all participants in the human physiology laboratory. Results:The mean age of participants was 48.13±12.12 years in the case group and 47.18±12.2 years in the control group. In hypothyroid tribal women, a significant decrease in conduction velocity was observed in the majority of motor nerves (right median [p = .03], left median [p = .02], left ulnar [p = .04], right posterior tibial [p = .001], left posterior tibial [p = .0001]) and sensory nerves (right median [p =.005], right ulnar [p = .02], right sural [p = .001], and left sural [p = .02]). Conclusion:In newly diagnosed cases of hypothyroidism in tribal women, there is a risk of neuropathy that impacts both motor and sensory neurons. Therefore, it is crucial to initiate early diagnosis and immediate treatment to prevent additional neurological damage.

Assessing the Capability of Large Language Models in Naturopathy Consultation.

Background The rapid advancements in natural language processing have brought about the widespread use of large language models (LLMs) across various medical domains. However, their effectiveness in specialized fields, such as naturopathy, remains relatively unexplored. Objective The study aimed to assess the capability of freely available LLM chatbots in providing naturopathy consultations for various types of diseases and disorders. Methods Five free LLMs (viz., Gemini, Copilot, ChatGPT, Claude, and Perplexity) were used to converse with 20 clinical cases (simulation of real-world scenarios). Each case had the case details and questions pertinent to naturopathy. The responses were presented to three naturopathy doctors with > 5 years of practice. The answers were rated by them on a five-point Likert-like scale for language fluency, coherence, accuracy, and relevancy. The average of these four attributes is termed perfection in his study. Results The overall score of the LLMs were Gemini 3.81±0.23, Copilot 4.34±0.28, ChatGPT 4.43±0.2, Claude 3.8±0.26, and Perplexity 3.91±0.28 (ANOVA F [3.034, 57.64] = 33.47, P <0.0001. Together, they showed overall ~80% perfection in consultation. The average measure intraclass correlation coefficient among the LLMs for the overall score was 0.463 (95% CI = -0.028 to 0.76), P = 0.03. Conclusion Although the LLM chatbots could help in providing naturopathy and yoga treatment consultation with approximately an overall fair level of perfection, their solution to the user varies across different chatbots and there was very low reliability among them.

Assessing the Capability of ChatGPT, Google Bard, and Microsoft Bing in Solving Radiology Case Vignettes.

Background The field of radiology relies on accurate interpretation of medical images for effective diagnosis and patient care. Recent advancements in artificial intelligence (AI) and natural language processing have sparked interest in exploring the potential of AI models in assisting radiologists. However, limited research has been conducted to assess the performance of AI models in radiology case interpretation, particularly in comparison to human experts. Objective This study aimed to evaluate the performance of ChatGPT, Google Bard, and Bing in solving radiology case vignettes (Fellowship of the Royal College of Radiologists 2A [FRCR2A] examination style questions) by comparing their responses to those provided by two radiology residents. Methods A total of 120 multiple-choice questions based on radiology case vignettes were formulated according to the pattern of FRCR2A examination. The questions were presented to ChatGPT, Google Bard, and Bing. Two residents wrote the examination with the same questions in 3 hours. The responses generated by the AI models were collected and compared to the answer keys and explanation of the answers was rated by the two radiologists. A cutoff of 60% was set as the passing score. Results The two residents (63.33 and 57.5%) outperformed the three AI models: Bard (44.17%), Bing (53.33%), and ChatGPT (45%), but only one resident passed the examination. The response patterns among the five respondents were significantly different ( p = 0.0117). In addition, the agreement among the generative AI models was significant (intraclass correlation coefficient [ICC] = 0.628), but there was no agreement between the residents (Kappa = -0.376). The explanation of generative AI models in support of answer was 44.72% accurate. Conclusion Humans exhibited superior accuracy compared to the AI models, showcasing a stronger comprehension of the subject matter. All three AI models included in the study could not achieve the minimum percentage needed to pass an FRCR2A examination. However, generative AI models showed significant agreement in their answers where the residents exhibited low agreement, highlighting a lack of consistency in their responses.

Radiological Differential Diagnoses Based on Cardiovascular and Thoracic Imaging Patterns: Perspectives of Four Large Language Models.

Background Differential diagnosis in radiology is a critical aspect of clinical decision-making. Radiologists in the early stages may find difficulties in listing the differential diagnosis from image patterns. In this context, the emergence of large language models (LLMs) has introduced new opportunities as these models have the capacity to access and contextualize extensive information from text-based input. Objective The objective of this study was to explore the utility of four LLMs-ChatGPT3.5, Google Bard, Microsoft Bing, and Perplexity-in providing most important differential diagnoses of cardiovascular and thoracic imaging patterns. Methods We selected 15 unique cardiovascular ( n = 5) and thoracic ( n = 10) imaging patterns. We asked each model to generate top 5 most important differential diagnoses for every pattern. Concurrently, a panel of two cardiothoracic radiologists independently identified top 5 differentials for each case and came to consensus when discrepancies occurred. We checked the concordance and acceptance of LLM-generated differentials with the consensus differential diagnosis. Categorical variables were compared by binomial, chi-squared, or Fisher's exact test. Results A total of 15 cases with five differentials generated a total of 75 items to analyze. The highest level of concordance was observed for diagnoses provided by Perplexity (66.67%), followed by ChatGPT (65.33%) and Bing (62.67%). The lowest score was for Bard with 45.33% of concordance with expert consensus. The acceptance rate was highest for Perplexity (90.67%), followed by Bing (89.33%) and ChatGPT (85.33%). The lowest acceptance rate was for Bard (69.33%). Conclusion Four LLMs-ChatGPT3.5, Google Bard, Microsoft Bing, and Perplexity-generated differential diagnoses had high level of acceptance but relatively lower concordance. There were significant differences in acceptance and concordance among the LLMs. Hence, it is important to carefully select the suitable model for usage in patient care or in medical education.

Heart Rate Variability in Normotensive and Hypertensive Adults After a Year of Receiving Oxford/AstraZeneca COVID-19 Vaccine: A Cross-Sectional Observational Study.

Background and aim Heart rate variability (HRV) helps in assessing the autonomic nervous system's function, which has been implicated in cardiovascular disease risk. HRV has been found to be deranged in hypertension. In addition, studies have shown that COVID-19 infection and vaccination can affect HRV. However, the long-term effect of HRV on hypertension has not been explored after COVID-19 vaccination. The objective of this study was to observe the HRV in hypertensive adults after one year of receiving the Oxford/AstraZeneca COVID-19 vaccine and to compare it with normotensive adults. Methods The study included 105 normotensives (blood pressure below 120/80 mmHg) and 75 hypertensive participants who had received the Oxford/AstraZeneca COVID-19 vaccine one year prior. HRV was measured using the PowerLab system (ADInstruments) with the participants in a sitting posture. The HRV parameters assessed included the time domain, frequency domain, and nonlinear measures. Data were presented in descriptive and inferential statistical terms, and the parameters of two groups of individuals were compared by either an unpaired t-test or the Mann-Whitney U test. Results A total of 105 normotensive participants with a mean age of 42.51 ± 9.28 years and 75 hypertensive participants with a mean age of 44.24 ± 10.19 years comprised the sample (p=0.24). Normotensive individuals had a higher standard deviation of RR intervals, a higher coefficient of variation of RR intervals, a higher standard deviation of heart rate, and a higher percentage of successive differences in RR intervals in the time domain. They also had higher values of very low-frequency power, low-frequency (LF) power, and high-frequency (HF) power in the frequency domain. The LF/HF ratio was not significantly different between the two groups. In nonlinear analysis, SD2, a measure of long-term heart rate variability, was higher in normotensive individuals. Conclusion The Oxford/AstraZeneca COVID-19 vaccine did not have a significant effect on HRV parameters in normotensive and hypertensive adults one year after vaccination. However, changes in HRV parameters were observed between supine and standing positions, suggesting the importance of postural changes in HRV assessment.

ChatGPT in academic writing: Maximizing its benefits and minimizing the risks.

This review article explores the use of ChatGPT in academic writing and provides insights on how to utilize it judiciously. With the increasing popularity of AI-powered language models, ChatGPT has emerged as a potential tool for assisting writers in the research and writing process. We have provided a list of potential uses of ChatGPT by a novice researcher for getting help during research proposal preparation and manuscript writing. However, there are concerns regarding its reliability and potential risks associated with its use. The review highlights the importance of maintaining human judgment in the writing process and using ChatGPT as a complementary tool rather than a replacement for human effort. The article concludes with recommendations for researchers and writers to ensure responsible and effective use of ChatGPT in academic writing.

Methods of collecting and recording attendance of medical students in a classroom: A systematic review.

BACKGROUND: Recording the attendance of students is an important affair for schools, colleges, and universities. It is required to be compliant with the regulations set by the board or universities, as it helps to calculate cumulative attendance to determine the eligibility of a student for university examinations. Furthermore, it is crucial for the administration to have a record of who is on campus in case of an emergency. However, literature on various attendance methods that are in practice around the world is scarce. Hence, this review aimed to conduct a systematic literature review to find the current methods of recording attendance in a classroom. MATERIALS AND METHODS: Relevant literature was searched in PubMed using the keywords "attendance," "method," and "classroom," and cross-references were also checked. Gray literature was also searched. The studies were screened for their content to determine their relevance to this review. A total of eight articles were included in the final systematic review. RESULTS: The literature suggests that the methods of recording attendance in a classroom can be broadly divided into three categories: teacher-led, student-led, and automated methods. In teacher-led methods, the teacher takes the responsibility of recording the attendance by actively implementing the method, and students respond (e.g., roll call by a teacher). In the student-led methods, the students take the responsibility to record attendance, while the teacher may act as a facilitator (e.g., signing in on a paper circulated among students). The newly introduced automated method requires little intervention from the teacher or students (e.g., radio-frequency identification (RFID)-based attendance). CONCLUSION: This review discusses the attendance recording methods in a classroom and explores the characteristics of these methods, along with their major advantages and disadvantages. Prospective teachers can gain insight into these methods and adopt suitable ones appropriate for their classrooms after assessing their suitability using the evaluation method suggested in this review.