Purpose: To develop and evaluate a large language model (LLM)-based learning tool, featuring virtual patients (VPs) and virtual assessors (VAs), and to assess its impact on medical students’ perceptions of history-taking education compared to conventional learning methods. Methods: A tool using the GPT-4 API was developed to provide seven clinical VP scenarios and a VA that delivered both immediate, reflective dialogue and comprehensive written feedback. First- and second-year medical students participated in a 6-day study. Pre- and post-participation surveys using a 5-point Likert scale assessed perceptions of the LLM tool versus conventional methods across usability, self-efficacy, and feedback quality domains. Results: Twenty-one students completed the study. The LLM-based tool demonstrated statistically significant improvements over conventional methods in all assessed domains. Students reported greater comfort during practice (mean 4.57 vs. 2.95, p=0.0002). Significant gains were seen in six of eight self-efficacy measures, including confidence in handling unfamiliar cases (4.00 vs. 2.90, p=0.0002). All nine feedback quality dimensions improved significantly, with feedback perceived as more specific (4.43 vs. 3.24, p=0.0005) and personalized (4.19 vs. 3.19, p=0.0001). Conclusion An LLM-based learning tool featuring VPs and VAs can significantly enhance medical students’ perceived learning experience in history-taking education. It offers a scalable, accessible, and cost-effective complementary training method. Future research should validate these subjective improvements with objective performance metrics.

Purpose: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL). Materials and Methods: Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates. Results: Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination. Conclusion In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.

Purpose: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL). Materials and Methods: Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates. Results: Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination. Conclusion In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.

Background: Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines. Methods: We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed. Results: A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%). Conclusion Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.

Background: Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines. Methods: We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed. Results: A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%). Conclusion Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.

Background: Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines. Methods: We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed. Results: A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%). Conclusion Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.

Background: Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines. Methods: We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed. Results: A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%). Conclusion Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.

Purpose: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL). Materials and Methods: Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates. Results: Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination. Conclusion In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.

Background: Hepatitis A is an acute type of hepatitis caused by the hepatitis A virus (HAV), which is mainly transmitted through the fecal-oral route and also rarely through blood transfusion. Studies on the seroprevalence of HAV immunoglobulin G in blood donors are necessary to predict the risk of releasing HAV infected blood products during epidemic and to establish effective transfusion recipient safety strategies. However, studies on the seroprevalence of HAV IgG in Korean blood donors have been rare. Methods: Blood samples from 10,000 plasmapheresis donors collected from 6th March to 23rd May 2024, were tested for the HAV IgG and analyzed based on age, region, and gender. The test was performed by chemiluminescence microparticle immunoassay on an Alinity i system (Abbott Laboratories, Lake Forest, IL, USA). Results: The overall seroprevalence of HAV IgG was 57.6% (5,759/10,000) for the research subjects. Significant differences between age cohorts or gender were found (16∼19 years, 65.6%; 20∼29 years, 65.5%; 30∼39 years, 28.4%; 40∼49 years, 41.5%; 50∼59 years, 82.6%; >60 years, 97.9% and Men, 59.6%; Women, 51.5%). However, there was no significant difference according to region. Conclusion To prepare for a potential outbreak of HAV, it is recommended that HAV vaccination be prioritized for Korean blood donors in their 30s and 40s, alongside the continued surveillance of HAV IgG seroprevalence among them.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.