Increased body fat is associated with decreased level of VO in young adults. Obesity in terms of Fat% is a better parameter than BMI for prediction of low VO.

BackgroundArtificial intelligence (AI) is evolving for healthcare services. Higher cognitive thinking in AI refers to the ability of the system to perform advanced cognitive processes, such as problem-solving, decision-making, reasoning, and perception. This type of thinking goes beyond simple data processing and involves the ability to understand and manipulate abstract concepts, interpret, and use information in a contextually relevant way, and generate new insights based on past experiences and accumulated knowledge. Natural language processing models like ChatGPT is a conversational program that can interact with humans to provide answers to queries. ObjectiveWe aimed to ascertain the capability of ChatGPT in solving higher-order reasoning in the subject of pathology. MethodsThis cross-sectional study was conducted on the internet using an AI-based chat program that provides free service for research purposes. The current version of ChatGPT (January 30 version) was used to converse with a total of 100 higher-order reasoning queries. These questions were randomly selected from the question bank of the institution and categorized according to different systems. The responses to each question were collected and stored for further analysis. The responses were evaluated by three expert pathologists on a zero to five scale and categorized into the structure of the observed learning outcome (SOLO) taxonomy categories. The score was compared by a one-sample median test with hypothetical values to find its accuracy. ResultA total of 100 higher-order reasoning questions were solved by the program in an average of 45.31±7.14 seconds for an answer. The overall median score was 4.08 (Q1-Q3: 4-4.33) which was below the hypothetical maximum value of five (one-test median test p <0.0001) and similar to four (one-test median test p = 0.14). The majority (86%) of the responses were in the "relational" category in the SOLO taxonomy. There was no difference in the scores of the responses for questions asked from various organ systems in the subject of Pathology (Kruskal Wallis p = 0.55). The scores rated by three pathologists had an excellent level of interrater reliability (ICC = 0.975 [95% CI: 0.965-0.983]; F = 40.26; p < 0.0001). ConclusionThe capability of ChatGPT to solve higher-order reasoning questions in pathology had a relational level of accuracy. Hence, the text output had connections among its parts to provide a meaningful response. The answers from the program can score approximately 80%. Hence, academicians or students can get help from the program for solving reasoning-type questions also. As the program is evolving, further studies are needed to find its accuracy level in any further versions.

Survey is an effective way of collecting data from a large sample within a short time. Due to increased use of digital medium, online survey is gaining popularity among researchers. It enables the surveyor to collect data from any corner of the world. Though the response rate is lower than physical survey, online survey has several advantages. Hence, we felt the necessity of sharing the basics of creating an online survey with budding researchers. Google form is an online survey platform which provides the service of creating survey forms, receiving responses and analysis of data free of cost. We used this platform to describe an example of conversion of a physical questionnaire to an online form. Hope this miniature technical guide help the readers to know the basic skills to create a form online for their future studies.

BackgroundDrug-drug interactions (DDIs) can have serious consequences for patient health and well-being. Patients who are taking multiple medications may be at an increased risk of experiencing adverse events or drug toxicity if they are not aware of potential interactions between their medications. Many times, patients selfprescribe medications without knowing DDI. ObjectiveThe objective is to investigate the effectiveness of ChatGPT, a large language model, in predicting and explaining common DDIs. MethodsA total of 40 DDIs lists were prepared from previously published literature. This list was used to converse with ChatGPT with a two-stage question. The first question was asked as "can I take X and Y together?" with two drug names. After storing the output, the next question was asked. The second question was asked as "why should I not take X and Y together?" The output was stored for further analysis. The responses were checked by two pharmacologists and the consensus output was categorized as "correct" and "incorrect." The "correct" ones were further classified as "conclusive" and "inconclusive." The text was checked for reading ease scores and grades of education required to understand the text. Data were tested by descriptive and inferential statistics. ResultsAmong the 40 DDI pairs, one answer was incorrect in the first question. Among correct answers, 19 were conclusive and 20 were inconclusive. For the second question, one answer was wrong. Among correct answers, 17 were conclusive and 22 were inconclusive. The mean Flesch reading ease score was 27.64±10.85 in answers to the first question and 29.35±10.16 in answers to the second question, p = 0.47. The mean Flesh-Kincaid grade level was 15.06±2.79 in answers to the first question and 14.85±1.97 in answers to the second question, p = 0.69. When we compared the reading levels with hypothetical 6th grade, the grades were significantly higher than expected (t = 20.57, p < 0.0001 for first answers and t = 28.43, p < 0.0001 for second answers). ConclusionChatGPT is a partially effective tool for predicting and explaining DDIs. Patients, who may not have immediate access to the healthcare facility for getting information about DDIs, may take help from ChatGPT. However, on several occasions, it may provide incomplete guidance. Further improvement is required for potential usage by patients for getting ideas about DDI.

Background and objective ChatGPT is an artificial intelligence (AI) language model that has been trained to process and respond to questions across a wide range of topics. It is also capable of solving problems in medical educational topics. However, the capability of ChatGPT to accurately answer first- and second-order knowledge questions in the field of microbiology has not been explored so far. Hence, in this study, we aimed to analyze the capability of ChatGPT in answering first- and second-order questions on the subject of microbiology. Materials and methods Based on the competency-based medical education (CBME) curriculum of the subject of microbiology, we prepared a set of first-order and second-order questions. For the total of eight modules in the CBME curriculum for microbiology, we prepared six first-order and six second-order knowledge questions according to the National Medical Commission-recommended CBME curriculum, amounting to a total of (8 x 12) 96 questions. The questions were checked for content validity by three expert microbiologists. These questions were used to converse with ChatGPT by a single user and responses were recorded for further analysis. The answers were scored by three microbiologists on a rating scale of 0-5. The average of three scores was taken as the final score for analysis. As the data were not normally distributed, we used a non-parametric statistical test. The overall scores were tested by a one-sample median test with hypothetical values of 4 and 5. The scores of answers to first-order and second-order questions were compared by the Mann-Whitney U test. Module-wise responses were tested by the Kruskall-Wallis test followed by the post hoc test for pairwise comparisons. Results The overall score of 96 answers was 4.04 ±0.37 (median: 4.17, Q1-Q3: 3.88-4.33) with the mean score of answers to first-order knowledge questions being 4.07 ±0.32 (median: 4.17, Q1-Q3: 4-4.33) and that of answers to second-order knowledge questions being 3.99 ±0.43 (median: 4, Q1-Q3: 3.67-4.33) (Mann-Whitney p=0.4). The score was significantly below the score of 5 (one-sample median test p<0.0001) and similar to 4 (one-sample median test p=0.09). Overall, there was a variation in median scores obtained in eight categories of topics in microbiology, indicating inconsistent performance in different topics. Conclusion The results of the study indicate that ChatGPT is capable of answering both first- and second-order knowledge questions related to the subject of microbiology. The model achieved an accuracy of approximately 80% and there was no difference between the model's capability of answering first-order questions and second-order knowledge questions. The findings of this study suggest that ChatGPT has the potential to be an effective tool for automated question-answering in the field of microbiology. However, continued improvement in the training and development of language models is necessary to enhance their per...