Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Purpose: This study compared the differences in health and dietary characteristics according to the subjective health perception among postmenopausal middle-aged women. Methods: Data from the 8 th Korea National Health and Nutrition Examination Survey (2019–2021) were utilized. The participants were naturally postmenopausal women aged 45–59 years, categorized into three groups (good, moderate, and bad) based on their subjective health perception. The general and biochemical characteristics, prevalence of diseases, mental health indicators, dietary behavior factors, food groups, and nutrient intake were compared according to subjective health perception. Results: Bad subjective health perception was associated with lower education levels, not engaging in economic activity, and higher rates of alcohol drinking and smoking. Women with bad subjective health perception had higher fasting blood glucose levels, hemoglobin A1c levels, blood insulin concentrations, and triglyceride concentrations, as well as lower total cholesterol and high-density lipoprotein cholesterol concentrations. In addition, the prevalence of hyperlipidemia and anemia was higher in this group. Women with bad subjective health perceptions were more likely to perceive themselves as fat or thin, experience activity restrictions, perceive stress, have suicidal ideation, and have sought medical assistance for mental issues. They also had higher rates of skipping lunch, lower frequency of fruit consumption, engaging in dietary therapy, feeling chewing discomfort, and higher total daily energy intake. Conclusion These findings suggest that bad subjective health perception in postmenopausal middle-aged women is associated with a higher prevalence of diseases, worse mental health status, and less healthy dietary behaviors. These results can serve as foundational data for future guidelines on desirable health and dietary behaviors aimed at improving the subjective health perceptions of middle-aged women after menopause.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

This article outlines the systematic radiological approach preoperative evaluation of perihilar cholangiocarcinoma (pCCA) using CT and MRI to provide key information regarding the suitability for curative surgical resection. It discusses older classification systems (BismuthCorlette, Memorial Sloan Kettering Cancer Center T staging) and follows the Korean Society of Abdomi­nal Radiology 2019 consensus recommendations for step-by-step assessment.The correlation between radiological, surgical, and pathological findings is illustrated through a pictorial review of pathologically proven cases. Benign and malignant mimics of pCCA are included to provide a comprehensive overview.

Purpose: This study compared the differences in health and dietary characteristics according to the subjective health perception among postmenopausal middle-aged women. Methods: Data from the 8 th Korea National Health and Nutrition Examination Survey (2019–2021) were utilized. The participants were naturally postmenopausal women aged 45–59 years, categorized into three groups (good, moderate, and bad) based on their subjective health perception. The general and biochemical characteristics, prevalence of diseases, mental health indicators, dietary behavior factors, food groups, and nutrient intake were compared according to subjective health perception. Results: Bad subjective health perception was associated with lower education levels, not engaging in economic activity, and higher rates of alcohol drinking and smoking. Women with bad subjective health perception had higher fasting blood glucose levels, hemoglobin A1c levels, blood insulin concentrations, and triglyceride concentrations, as well as lower total cholesterol and high-density lipoprotein cholesterol concentrations. In addition, the prevalence of hyperlipidemia and anemia was higher in this group. Women with bad subjective health perceptions were more likely to perceive themselves as fat or thin, experience activity restrictions, perceive stress, have suicidal ideation, and have sought medical assistance for mental issues. They also had higher rates of skipping lunch, lower frequency of fruit consumption, engaging in dietary therapy, feeling chewing discomfort, and higher total daily energy intake. Conclusion These findings suggest that bad subjective health perception in postmenopausal middle-aged women is associated with a higher prevalence of diseases, worse mental health status, and less healthy dietary behaviors. These results can serve as foundational data for future guidelines on desirable health and dietary behaviors aimed at improving the subjective health perceptions of middle-aged women after menopause.

This article outlines the systematic radiological approach preoperative evaluation of perihilar cholangiocarcinoma (pCCA) using CT and MRI to provide key information regarding the suitability for curative surgical resection. It discusses older classification systems (BismuthCorlette, Memorial Sloan Kettering Cancer Center T staging) and follows the Korean Society of Abdomi­nal Radiology 2019 consensus recommendations for step-by-step assessment.The correlation between radiological, surgical, and pathological findings is illustrated through a pictorial review of pathologically proven cases. Benign and malignant mimics of pCCA are included to provide a comprehensive overview.

Objective: To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. Materials and Methods: A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data—were independently extracted by two reviewers, and adherence was calculated for each item. Results: Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Conclusion Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

Purpose: This study compared the differences in health and dietary characteristics according to the subjective health perception among postmenopausal middle-aged women. Methods: Data from the 8 th Korea National Health and Nutrition Examination Survey (2019–2021) were utilized. The participants were naturally postmenopausal women aged 45–59 years, categorized into three groups (good, moderate, and bad) based on their subjective health perception. The general and biochemical characteristics, prevalence of diseases, mental health indicators, dietary behavior factors, food groups, and nutrient intake were compared according to subjective health perception. Results: Bad subjective health perception was associated with lower education levels, not engaging in economic activity, and higher rates of alcohol drinking and smoking. Women with bad subjective health perception had higher fasting blood glucose levels, hemoglobin A1c levels, blood insulin concentrations, and triglyceride concentrations, as well as lower total cholesterol and high-density lipoprotein cholesterol concentrations. In addition, the prevalence of hyperlipidemia and anemia was higher in this group. Women with bad subjective health perceptions were more likely to perceive themselves as fat or thin, experience activity restrictions, perceive stress, have suicidal ideation, and have sought medical assistance for mental issues. They also had higher rates of skipping lunch, lower frequency of fruit consumption, engaging in dietary therapy, feeling chewing discomfort, and higher total daily energy intake. Conclusion These findings suggest that bad subjective health perception in postmenopausal middle-aged women is associated with a higher prevalence of diseases, worse mental health status, and less healthy dietary behaviors. These results can serve as foundational data for future guidelines on desirable health and dietary behaviors aimed at improving the subjective health perceptions of middle-aged women after menopause.

This article outlines the systematic radiological approach preoperative evaluation of perihilar cholangiocarcinoma (pCCA) using CT and MRI to provide key information regarding the suitability for curative surgical resection. It discusses older classification systems (BismuthCorlette, Memorial Sloan Kettering Cancer Center T staging) and follows the Korean Society of Abdomi­nal Radiology 2019 consensus recommendations for step-by-step assessment.The correlation between radiological, surgical, and pathological findings is illustrated through a pictorial review of pathologically proven cases. Benign and malignant mimics of pCCA are included to provide a comprehensive overview.