Specialty Exam Research Articles

Assessing the Impact of ChatGPT in Dermatology: A Comprehensive Rapid Review.

Background/Objectives: The use of artificial intelligence (AI) in dermatology is expanding rapidly, with ChatGPT, a large language model (LLM) from OpenAI, showing promise in patient education, clinical decision-making, and teledermatology. Despite its potential, the ethical, clinical, and practical implications of its application remain insufficiently explored. This study aims to evaluate the effectiveness, challenges, and future prospects of ChatGPT in dermatology, focusing on clinical applications, patient interactions, and medical writing. ChatGPT was selected due to its broad adoption, extensive validation, and strong performance in dermatology-related tasks. Methods: A thorough literature review was conducted, focusing on publications related to ChatGPT and dermatology. The search included articles in English from November 2022 to August 2024, as this period captures the most recent developments following the launch of ChatGPT in November 2022, ensuring that the review includes the latest advancements and discussions on its role in dermatology. Studies were chosen based on their relevance to clinical applications, patient interactions, and ethical issues. Descriptive metrics, such as average accuracy scores and reliability percentages, were used to summarize study characteristics, and key findings were analyzed. Results: ChatGPT has shown significant potential in passing dermatology specialty exams and providing reliable responses to patient queries, especially for common dermatological conditions. However, it faces limitations in diagnosing complex cases like cutaneous neoplasms, and concerns about the accuracy and completeness of its information persist. Ethical issues, including data privacy, algorithmic bias, and the need for transparent guidelines, were identified as critical challenges. Conclusions: While ChatGPT has the potential to significantly enhance dermatological practice, particularly in patient education and teledermatology, its integration must be cautious, addressing ethical concerns and complementing, rather than replacing, dermatologist expertise. Future research should refine ChatGPT's diagnostic capabilities, mitigate biases, and develop comprehensive clinical guidelines.

Performance of ChatGPT in the In-Training Examination for Anesthesiology and Pain Medicine Residents in South Korea: Observational Study.

ChatGPT has been tested in health care, including the US Medical Licensing Examination and specialty exams, showing near-passing results. Its performance in the field of anesthesiology has been assessed using English board examination questions; however, its effectiveness in Korea remains unexplored. This study investigated the problem-solving performance of ChatGPT in the fields of anesthesiology and pain medicine in the Korean language context, highlighted advancements in artificial intelligence (AI), and explored its potential applications in medical education. We investigated the performance (number of correct answers/number of questions) of GPT-4, GPT-3.5, and CLOVA X in the fields of anesthesiology and pain medicine, using in-training examinations that have been administered to Korean anesthesiology residents over the past 5 years, with an annual composition of 100 questions. Questions containing images, diagrams, or photographs were excluded from the analysis. Furthermore, to assess the performance differences of the GPT across different languages, we conducted a comparative analysis of the GPT-4's problem-solving proficiency using both the original Korean texts and their English translations. A total of 398 questions were analyzed. GPT-4 (67.8%) demonstrated a significantly better overall performance than GPT-3.5 (37.2%) and CLOVA-X (36.7%). However, GPT-3.5 and CLOVA X did not show significant differences in their overall performance. Additionally, the GPT-4 showed superior performance on questions translated into English, indicating a language processing discrepancy (English: 75.4% vs Korean: 67.8%; difference 7.5%; 95% CI 3.1%-11.9%; P=.001). This study underscores the potential of AI tools, such as ChatGPT, in medical education and practice but emphasizes the need for cautious application and further refinement, especially in non-English medical contexts. The findings suggest that although AI advancements are promising, they require careful evaluation and development to ensure acceptable performance across diverse linguistic and professional settings.

RURAL OBSTACLES AND CAREER ANXIETIES: VOCATIONAL CONCERNS AMONG MEDICAL INTERNS

Medical students face a multitude of challenges including rigorous exams, exposure to diseases and death, and the stress and anxiety associated with preparing for the Medical Specialty Exam (MSE). We aimed to determine what are the primary reasons for vocational future concerns among intern doctors, and are there gender differences in the causes and levels of the concerns. This cross-sectional study was conducted at Ondokuz Mayıs University Faculty of Medicine between July 2019 and March 2020, and 148 interns whose informed consent was obtained were included in the study. The data were collected by 5-point Likert type scale for interns' future concerns, consisted of 15 items, and 3 subdimensions. The Cronbach’s alpha coefficient of the scale was 0.89. Of the 148 participants with a mean age of 23.9 years, 43.9% were male, and 56.1% were female. The main reasons for concern were "inability to prepare for the medical specialty exam (68%)" and "failure to pass the MSE (58%)". These had been followed by concerns about “compulsory service in the eastern regions", “dealing with medical emergencies”, and other professional incompetence. Few interns reported that they were worried about being unemployed (13%). We determined that the vocational concerns of the females were significantly higher than the males, especially about working in the eastern regions, medical emergencies, and clinical skills (p

Imaging Informatics Education in Clinical Informatics Programs: Perspective from Imaging and Clinical Informatics Professionals.

Imaging and Clinical Informatics are domains of biomedical informatics. Imaging Informatics topics are often not covered in depth in most Clinical Informatics fellowships. While dedicated Imaging Informatics fellowships exist, they may not have the same rigor as ACGME (Accreditation Council for Graduate Medical Education) accredited Clinical Informatics fellowships and they do not provide a direct path toward subspecialty board certification. We compared published curricula and test content between Clinical and Imaging Informatics fellowship programs. We then highlighted differences between training programs and identified overlapping topics and opportunities for additional education for each type of trainee. Published consensus curricula and topics were extracted for each specialty. Two informaticists compared topics as shared or not shared between specialties. Next, test content outlines were compared for each specialty exam, extracted, and classified as shared or not shared content. A Venn diagram was created to highlight areas unique to each specialty as well as areas of overlap. There were 139 Clinical Informatics topics compared with 97 Imaging Informatics topics. Of the 139 Clinical Informatics topics, 115 (83%) were covered in the Imaging Informatics curriculum. Of the 97 Imaging Informatics topics, 74 (76%) were covered in the Clinical Informatics curriculum. When using test content outline data, 170 out of 397 (43%) Imaging Informatics topics matched to 64 out of 139 (46%) Clinical Informatics topics. We describe examples of overlapping topics and those unique to each program to identify potential areas to expand. Imaging Informatics and Clinical Informatics fellowship programs have some overlap with areas unique to each. Our review may help guide those seeking informatics education and potential certification. As enterprise imaging evolves, these differences may become more important and create knowledge gaps, if not systematically evaluated.

The potential of ChatGPT in medicine: an example analysis of nephrology specialty exams in Poland.

In November 2022, OpenAI released a chatbot named ChatGPT, a product capable of processing natural language to create human-like conversational dialogue. It has generated a lot of interest, including from the scientific community and the medical science community. Recent publications have shown that ChatGPT can correctly answer questions from medical exams such as the United States Medical Licensing Examination and other specialty exams. To date, there have been no studies in which ChatGPT has been tested on specialty questions in the field of nephrology anywhere in the world. Using the ChatGPT-3.5 and -4.0 algorithms in this comparative cross-sectional study, we analysed 1560 single-answer questions from the national specialty exam in nephrology from 2017 to 2023 that were available in the Polish Medical Examination Center's question database along with answer keys. Of the 1556 questions posed to ChatGPT-4.0, correct answers were obtained with an accuracy of 69.84%, compared with ChatGPT-3.5 (45.70%, P=.0001) and with the top results of medical doctors (85.73%, P=.0001). Of the 13 tests, ChatGPT-4.0 exceeded the required ≥60% pass rate in 11 tests passed, and scored higher than the average of the human exam results. ChatGPT-3.5 was not spectacularly successful in nephrology exams. The ChatGPT-4.0 algorithm was able to pass most of the analysed nephrology specialty exams. New generations of ChatGPT achieve similar results to humans. The best results of humans are better than those of ChatGPT-4.0.

Generative pretrained transformer-4, an artificial intelligence text predictive model, has a high capability for passing novel written radiology exam questions.

AI-image interpretation, through convolutional neural networks, shows increasing capability within radiology. These models have achieved impressive performance in specific tasks within controlled settings, but possess inherent limitations, such as the inability to consider clinical context. We assess the ability of large language models (LLMs) within the context of radiology specialty exams to determine whether they can evaluate relevant clinical information. A database of questions was created with official sample, author written, and textbook questions based on the Royal College of Radiology (United Kingdom) FRCR 2A and American Board of Radiology (ABR) Certifying examinations. The questions were input into the Generative Pretrained Transformer (GPT) versions 3 and 4, with prompting to answer the questions. One thousand seventy-two questions were evaluated by GPT-3 and GPT-4. 495 (46.2%) were for the FRCR 2A and 577 (53.8%) were for the ABR exam. There were 890 single best answers (SBA), and 182 true/false questions. GPT-4 was correct in 629/890 (70.7%) SBA and 151/182 (83.0%) true/false questions. There was no degradation on author written questions. GPT-4 performed significantly better than GPT-3 which selected the correct answer in 282/890 (31.7%) SBA and 111/182 (61.0%) true/false questions. Performance of GPT-4 was similar across both examinations for all categories of question. The newest generation of LLMs, GPT-4, demonstrates high capability in answering radiology exam questions. It shows marked improvement from GPT-3, suggesting further improvements in accuracy are possible. Further research is needed to explore the clinical applicability of these AI models in real-world settings.

Performance of ChatGPT incorporated chain-of-thought method in bilingual nuclear medicine physician board examinations.

This research explores the performance of ChatGPT, compared to human doctors, in bilingual, Mandarin Chinese and English, medical specialty exam in Nuclear Medicine in Taiwan. The study employed generative pre-trained transformer (GPT-4) and integrated chain-of-thoughts (COT) method to enhance performance by triggering and explaining the thinking process to answer the question in a coherent and logical manner. Questions from the Taiwanese Nuclear Medicine Specialty Exam served as the basis for testing. The research analyzed the correctness of AI responses in different sections of the exam and explored the influence of question length and language proportion on accuracy. AI, especially ChatGPT with COT, exhibited exceptional capabilities in theoretical knowledge, clinical medicine, and handling integrated questions, often surpassing, or matching human doctor performance. However, AI struggled with questions related to medical regulations. The analysis of question length showed that questions within the 109-163 words range yielded the highest accuracy. Moreover, an increase in the proportion of English words in questions improved both AI and human accuracy. This research highlights the potential and challenges of AI in the medical field. ChatGPT demonstrates significant competence in various aspects of medical knowledge. However, areas like medical regulations require improvement. The study also suggests that AI may help in evaluating exam question difficulty and maintaining fairness in examinations. These findings shed light on AI role in the medical field, with potential applications in healthcare education, exam preparation, and multilingual environments. Ongoing AI advancements are expected to further enhance AI utility in the medical domain.

Performance of ChatGPT and GPT-4 on Polish National Specialty Exam (NSE) in Ophthalmology

Abstract Introduction Artificial intelligence (AI) has evolved significantly, driven by advancements in computing power and big data. Technologies like machine learning and deep learning have led to sophisticated models such as GPT-3.5 and GPT-4. This study assesses the performance of these AI models on the Polish National Specialty Exam in ophthalmology, exploring their potential to support research, education, and clinical decision-making in healthcare. Materials and Methods The study analyzed 98 questions from the Spring 2023 Polish NSE in Ophthalmology. Questions were categorized into five groups: Physiology & Diagnostics, Clinical & Case Questions, Treatment & Pharmacology, Surgery, and Pediatrics. GPT-3.5 and GPT-4 were tested for their accuracy in answering these questions, with a confidence rating from 1 to 5 assigned to each response. Statistical analyses, including the Chi-squared test and Mann-Whitney U test, were employed to compare the models’ performance. Results GPT-4 demonstrated a significant improvement over GPT-3.5, correctly answering 63.3% of questions compared to GPT-3.5’s 37.8%. GPT-4’s performance met the passing criteria for the NSE. The models showed varying degrees of accuracy across different categories, with a notable gap in fields like surgery and pediatrics. Conclusions The study highlights the potential of GPT models in aiding clinical decisions and educational purposes in ophthalmology. However, it also underscores the models’ limitations, particularly in specialized fields like surgery and pediatrics. The findings suggest that while AI models like GPT-3.5 and GPT-4 can significantly assist in the medical field, they require further development and fine-tuning to address specific challenges in various medical domains.

Uzman Hekimlerin Bağlanma Stilleri ve Bağlanma Stillerinin Uzmanlık Dalıyla İlişkisi

Objective: The aim of this study is to evaluate the attachment styles of specialist physicians and to examine the differences in attachment styles according to their specialty and whether they are placed in their first preference in the medical specialty exam (TUS).
 Method: A total of 92 physicians were included in the study. Individual information collection forms and the Relationship Scales Questionnaire (RSQ) were administered to all participants. Participants were asked about their gender, their specialization field, and whether their current specialization field was their first choice in the Medical Specialization Exam (TUS). Specialization fields were divided into 4 groups: surgical specialties, internal medicine specialties, basic medical sciences, and family medicine specialization.
 Results: Among all participants, 14.1% exhibited secure attachment, 57.6% displayed fearful attachment, 32.6% demonstrated preoccupied attachment, and 3.3% had dismissive attachment. There was no significant difference in attachment styles based on the participants' genders. There was no significant difference in attachment styles based on whether they entered their first choice in the Medical Specialization Exam (TUS). There was no significant difference in attachment styles based on the specialization fields in which the participants received their education.
 Conclusion: Among expert physicians, anxious attachment was the most common attachment style. Although there was no statistically significant difference in attachment styles based on physicians' specialization fields, family medicine and internal medicine practitioners exhibited a higher rate of secure attachment compared to those in the basic medical and surgical sciences. Determination of other possible motivational factors as well as attachment styles could provide guidance and insight to the physician in this choice that will affect her/his whole life

ChatGPT performance in the medical specialty exam: An observational study.

In our study, we aimed to evaluate the success of ChatGPT by determining its performance in the last 5 medical specialty exams (MSE) conducted and its ranking among the candidates of that year, and to determine its potential use in healthcare services. Publicly available MSE questions and answer keys from the last 5 years were scanned, a total of 1177 questions were included in the study, all questions were asked to the ChatGPT (OpenAI; San Francisco, CA) GPT-3.5 series, which is the March 23, 2023 version. The average score and rank that ChatGPT would receive if it had entered the exam that year were determined. Questions were categorized as short question group, long question group, single select multiple-choice questions, and multi-select multiple-choice questions. The lowest success proportion was determined as 54.3%, and the highest success proportion was 70.9% correct answer percentage. It achieved a sufficient result as 1787th out of 22,214 people in its most successful exam, and 4428th out of 21,476 participants in its least successful one. No statistically significant difference was found between the correct answers it gave to clinical and basic science questions (P: .66). ChatGPT statistically significantly answered a higher proportion of questions correctly in the short questions group compared to the long questions group (P = .03), and in the single select multiple choice questions group compared to the multi-select multiple choice questions group (P < .001). ChatGPT has been successful in the MSE, a challenging exam for doctors in our country. However, it is a fact that ChatGPT is still behind the expert in the field for now, and what will happen with program developments in the future is a matter of curiosity for all of us.

Will ChatGPT pass the Polish specialty exam in radiology and diagnostic imaging? Insights into strengths and limitations.

Rapid development of artificial intelligence has aroused curiosity regarding its potential applications in medical field. The purpose of this article was to present the performance of ChatGPT, a state-of-the-art language model in relation to pass rate of national specialty examination (PES) in radiology and imaging diagnostics within Polish education system. Additionally, the study aimed to identify the strengths and limitations of the model through a detailed analysis of issues raised by exam questions. The present study utilized a PES exam consisting of 120 questions, provided by Medical Exami-nations Center in Lodz. Questions were administered using openai.com platform that grants free access to GPT-3.5 model. All questions were categorized according to Bloom's taxonomy to assess their complexity and difficulty. Following the answer to each exam question, ChatGPT was asked to rate its confidence on a scale of 1 to 5 to evaluate the accuracy of its response. ChatGPT did not reach the pass rate threshold of PES exam (52%); however, it was close in certain question categories. No significant differences were observed in the percentage of correct answers across question types and sub-types. The performance of the ChatGPT model in the pass rate of PES exam in radiology and imaging diagnostics in Poland is yet to be determined, which requires further research on improved versions of ChatGPT.

Classroom-Based Learning in an Academic Obstetrics and Gynecology Residency Training Program.

Classroom-based learning such as academic half days (AHDs) are complementary to workplace learning in postgraduate medical education. This study examined three research questions: the purpose of AHDs, elements of an effective AHD, and factors that make AHD sustainable. We conducted a case study of the AHD in a large Obstetrics and Gynecology residency program at the University of British Columbia. Residents were interviewed in 2013 (n = 11) and 2018 (n = 7) and the program administrator was interviewed in 2018. The themes in each research question were identified by modified inductive analysis. Residents expressed that the purposes of AHD included: providing organization and an overview for their knowledge acquisition; preparation for their Royal College specialty exam; and to provide a venue for peer support and mentorship. Elements of an effective AHD include the repetition of key concepts; formative assessments such as quizzes, a suitable balance of faculty input and resident active participation, and protection from clinical duty during AHD. Regarding the sustainability of AHD, themes included: addressing barriers to faculty participation, providing administrative support for logistical needs, and providing feedback to faculty. This work provides important insights into the purpose, effectiveness, and sustainability of AHDs for those who design and implement classroom learning for residents.

Correspondence to "Will ChatGPT pass the Polish specialty exam in radiology and diagnostic imaging?"

Correspondence to "Will ChatGPT pass the Polish specialty exam in radiology and diagnostic imaging?"

Depression, Occupational Anxiety, and Related Factors in Medical Students

Abstract Background: Depression occupies an important place in medical students’ psychiatric disorders, with a prevalence of approximately 20-40%. We aim to examine factors that might affect depression in medical students and investigate the relationship between occupational anxiety and depression severity. Methods: Eighty-eight medical students who were diagnosed as having major depressive disorder were included in the study. A Sociodemographic Data Form, the Occupational Anxiety Questionnaire for Medical Students, Beck Depression Inventory, and Beck Anxiety Inventory were used. Results: A statistically significant relationship was found between academic success, living with family, Occupational Anxiety Questionnaire for Medical Students and Beck Depression Inventory scores. The mean value of the occupational anxiety of the students was 35.87±9.82. The most anxious options for students in the Occupational Anxiety Questionnaire for Medical Students were the inability to prepare for and pass the Central Medical Specialty Exam. Conclusions: It is necessary to focus on factors that may affect the severity of depression, which affect both the social and professional lives of medical students who are physician candidates. The issues about occupational anxiety should be taken into consideration when planning medical school education and postgraduate working conditions.

Tıpta Uzmanlık Sınavı Temel Hekimliği Ne Kadar Ölçer?

Aim: Doctors who want to receive expertise training have to take the Medical Specialty Exam (TUS). That’s why they have to study for TUS in the last period of their undergraduate medical education. However, the content of TUS is different from the undergraduate medical education content (UÇEP) which is determined by the national core education program. This situation causes physicians preparing for TUS to ignore basic medical knowledge. This study aims to determine how much TUS, which is the main purpose of undergraduate medical education, measures basic medicine. Our study is the first research on this subject.Methods: In this study, a descriptive scanning model and document analysis method were used. The sample of the study consists of TUS clinical science questions belonging to the years 2009-2018. The 2009-2018 basic medical sciences TUS questions and all TUS questions asked in 2019-2020 were not included in the study. After the 2280 questions that could be reached from this sample were evaluated by a commission formed by the faculty members of Ankara Yıldırım Beyazıt University Faculty of Medicine, Department of Family Medicine, the data obtained were analyzed with the SPSS 23.0 package program.Results: In the study, 3 different faculty members were asked to score all questions between 0 and 9 according to the degree of basic medicine measurement. The average of the scores given by the three faculty members was found as 1.64 ± 3.02. All 3 faculty members gave 0 points to 1622 questions and 9, which is considered full points, to 177 questions. When the change in the average scores according to the exams was examined, it was seen that the highest average score was the spring TUS of 2009 with 3.2 points. The lowest score was the 2018 autumn TUS, with a score of 0.8. It was seen that the scores given by the three faculty members independently were compatible with each other and showed a strong positive correlation (r=0,970; p &amp;lt;0.001).Conclusions: Even though undergraduate education is tried to be standardized with the National Core Education Program (UCEP), as a result of our research, it has been seen that the content of the questions asked in TUS is incompatible with UÇEP and therefore basic medicine. As the years progressed, it was determined that the questions measuring basic medicine were replaced by questions containing detailed information of the branches. We think that this situation will constitute an obstacle for undergraduate medical education students to acquire fundamental medical proficiency.

A Research on the Factors Affecting the Preference of Medical Specialization Branches

Aim. The aim of this study is to determine the factors affecting the preferences of specialization in the field of medicine. Materials and Methods. Mixed research and exploratory sequential research design were used. In the exploratory phase, data were collected from specialist physicians (n=14) and findings were analyzed by descriptive and content analysis. In the light of qualitative findings, a measurement tool was developed and applied to medical school students and the physicians who prepared specialty exams (n = 502). Results. Qualitative findings were structured under 3 themes: individual, occupational and systemic factors. The measurement tool, which was named “Physicians' Preference Tendencies of Specialty Branch” was structured as 42 items and 7 dimensions: risk, comfort, health problems, status, emotional interest, gender, and marital status emerged. Conclusions. Although there are many factors that affect preferences of the medical field, it is concluded that personality traits and idealism of individuals and mortality rates associated with branch or field of medicine are the most significant professional factors, while the risks and the exposure to violence and the application of the additional payment based on the performance of candidates are the systemic factors that affect selections and preferences. Also, it was concluded that qualitative data obtained in the research were supported with quantitative data.

The relationship between exam anxiety, depression, and back pain in physicians preparing for the specialty exam.

This study aims at examining the possibility of anxiety and depression in physicians preparing for the specialty exam and the back pain caused by continuous working with these depressive symptoms. The research was started by getting approval from the local Ethics Committee of Gazi Yaşargil Training and Research Hospital. Socio-demographic data form, EQ-5D general quality of life scale, Oswestry low back pain disability scale, and Hospital anxiety and depression scale (HADS) were applied to volunteers. Among the physicians participating in our study, both anxiety and depression scores from the HADS scale scores applied to the exam preparation group were statistically significantly higher than those of students who did not prepare for the exam. In our study, the highness of the scale scores in the exam preparation group was statistically high. It should not be ignored that there may be complaints of anxiety, depression, and low back pain during the preparation period for the specialty examination in physicians. During the exams' periods, psychosocial support mechanisms should be activated, and active exercise activities should be recommended. For more detailed data, large-scale participatory studies are needed.

Virtual versus traditional learning during COVID-19: quantitative comparison of outcomes for two articulating ADN cohorts

AimThe study compared outcomes between articulating nursing student cohorts virtual learning during COVID-19 and traditional learning prior to COVID-19. BackgroundClinical reasoning is vital to developing clinical nursing judgment. During the COVID-19 pandemic, nursing schools replaced traditional classroom and in-hospital clinical with virtual classroom and clinical learning. DesignA quantitative retrospective cohort study compared outcomes of the nursing students virtual learning during COVID-19 (n = 18) with traditional learning methods before COVID-19 (n = 14) at one college in Texas. MethodOutcomes of the study were measured using a standardized Health Education Systems Inc. (HESI®) specialty exam, HESI® end of program (Exit) exam, and HESI® clinical judgment subscales. Independent-samples t-tests and Chi square were conducted to compare outcomes from virtual and traditional learning. ResultsNo statistically significant difference was observed between the traditional group (M = 752.93, SD = 148.88) and virtual learning group (M = 761.72, SD = 152.11); t(30) = −0.16, p = 0.87 was identified on the HESI® Maternal pediatric Specialty exam. No statistically significant differences were observed on the HESI® Exit exam or clinical judgment subscales; no relationship between the type of learning and NCLEX-RN® pass rates. ConclusionThe study findings are encouraging, yet limited, warranting further investigation of virtual learning.

Prevalence of irritable bowel syndrome and associated factors in intern doctors at a state university in western black sea, Turkey: A cross-sectional study

Irritable bowel syndrome (IBS) is a functional digestive disorder that affects medical students at a higher rate than the general population. Intern doctors are the most affected class by IBS among medical students. Determining the prevalence of IBS and associated risk factors is essential for taking preventive measures. We aimed to investigate the prevalence and predictors of IBS among intern doctors in this study. This study was conducted using an online survey for intern doctors in the 2021-2022 academic year. Rome IV criteria were utilized for IBS diagnosis. Anxiety and depression scores were calculated according to the Hospital Anxiety and Depression Scale (HADS). The prevalence of IBS in the 249 participants was 31.7% included in this study. 32.1% of all participants had depression, 44.2% had anxiety, 65.1% had sleep disorders, 73.1% had MSE (medical specialty exam) anxiety, and 96% were concerned about carrying out the medical profession after graduating from university. Female gender (p&lt;0.001), using regular medication (p=0.019), not exercising regularly (p=0.002), sleep disturbance (p&lt;0.001), food hypersensitivity (p=0.001), having a history of severe acute gastroenteritis (AGE) before IBS symptoms started (p&lt;0.001), IBS in first-degree relatives (p&lt;0.001), be low income (p&lt;0.001), having a divorced parent (p=0.008), not having siblings (p=0.002), severe emotional stress history in the last six months (p=0.004), having depression (p&lt;0.001), having anxiety (p&lt;0.001), and MSE anxiety (p&lt;0.001) identified as risk factors associated with IBS. The multivariant analysis determined independent risk factors affecting the prevalence of IBS in intern doctors as female gender, depression, anxiety, and severe AGE history before IBS symptoms started. In this study, it was determined that approximately one-third of the interns were diagnosed with IBS. Stress management training can be considered to screen this group for IBS and psychological problems and to help them cope with these stressors.

Impact of changing medical workforce demographics in renal medicine over 7 years: Analysis of GMC national trainee survey data

Increasing numbers of doctors in training are taking career breaks, with burnout cited as a potential cause. This study analysed General Medical Council (GMC) national training survey data (renal medicine) to understand the impacts of changing workforce demographics on trainee outcomes and wellbeing. Increasing proportions of female, Black, Asian and minority ethnic (BAME), and international medical graduates are entering the workforce. Specialty exam pass rates have fallen and are lower for BAME and international medical graduates in renal medicine. Time to complete higher specialty training has increased for female trainees. Self-reported burnout rates for renal trainees were higher than other medical specialties and highest for male BAME trainees. Burnout was only partially mitigated by less-than-full-time working, but had no impact on progression, sick-leave or time out of training. It is important to recognise changes to the workforce and proactively plan to effectively support a more diverse group of trainees, to enable them to succeed and reduce differential attainment.