Performance of generative pre-trained transformers (GPTs) in Certification Examination of the College of Family Physicians of Canada.

INTRODUCTION: The application of large language models such as generative pre-trained transformers (GPTs) has been promising in medical education, and its performance has been tested for different medical exams. This study aims to assess the performance of GPTs in responding to a set of sample questions of short-answer management problems (SAMPs) from the certification exam of the College of Family Physicians of Canada (CFPC). METHOD: Between August 8th and 25th, 2023, we used GPT-3.5 and GPT-4 in five rounds to answer a sample of 77 SAMPs questions from the CFPC website. Two independent certified family physician reviewers scored AI-generated responses twice: first, according to the CFPC answer key (ie, CFPC score), and second, based on their knowledge and other references (ie, Reviews' score). An ordinal logistic generalised estimating equations (GEE) model was applied to analyse repeated measures across the five rounds. RESULT: According to the CFPC answer key, 607 (73.6%) lines of answers by GPT-3.5 and 691 (81%) by GPT-4 were deemed accurate. Reviewer's scoring suggested that about 84% of the lines of answers provided by GPT-3.5 and 93% of GPT-4 were correct. The GEE analysis confirmed that over five rounds, the likelihood of achieving a higher CFPC Score Percentage for GPT-4 was 2.31 times more than GPT-3.5 (OR: 2.31; 95% CI: 1.53 to 3.47; p<0.001). Similarly, the Reviewers' Score percentage for responses provided by GPT-4 over 5 rounds were 2.23 times more likely to exceed those of GPT-3.5 (OR: 2.23; 95% CI: 1.22 to 4.06; p=0.009). Running the GPTs after a one week interval, regeneration of the prompt or using or not using the prompt did not significantly change the CFPC score percentage. CONCLUSION: In our study, we used GPT-3.5 and GPT-4 to answer complex, open-ended sample questions of the CFPC exam and showed that more than 70% of the answers were accurate, and GPT-4 outperformed GPT-3.5 in responding to the questions. Large language models such as GPTs seem promising for assisting candidates of the CFPC exam by providing potential answers. However, their use for family medicine education and exam preparation needs further studies.

Comparative study of Microsporum canis isolates by DNA fingerprinting.

Microsporum canis is a zoophilic fungus and it is an important agent of dermatophytosis. Cats act as important reservoirs. Clinically, it is too difficult to differentiate dermatophytosis caused by various species, also this fungus loses its morphological characteristics easily because of subculture; so using of rapid and accurate laboratory techniques for identifying the dermatophytes is important, therefore, RAPD-PCR was applied for the differentiation of the isolates. In this study, 10 M. canis isolates were detected in cats, dog, human, fox and rabbit at the Mycology Research Center, Faculty of Veterinary Medicine, University of Tehran. For running the RAPD-PCR, PCR set system and three random primers OPU 15, OPU 13 and OPA 04 were used. Then phylogenetic tree and similarity coefficient table were drawn. The results showed that there were some common bands between M. canis isolates. There were some specific bands for each isolates, as well. Our study showed, despite the typical morphology of the whole isolates, they were placed in different branches in molecular typing.