Background/Aims: Elevated gamma-glutamyl transferase (GGT) levels indicate hepatic dysfunction and have been linked to an increased risk of pancreatobiliary cancers. However, this association, particularly in individuals with diabetes mellitus (DM), requires elucidation. We aimed to examine the association between elevated serum GGT levels and pancreatobiliary cancer risk in patients with diabetes. Methods: Our study included data from the National Health Insurance Service (NHIS) database for 2,459,966 adults aged >20 years diagnosed with DM between 2009 and 2012. We examined the association between serum GGT levels and pancreatobiliary cancer risk, considering DMrelated factors. Serum GGT levels were categorized into quartiles, and Cox proportional hazards analysis was performed to evaluate the association between serum GGT levels and pancreatobiliary cancer risk. Results: Over a median follow-up period of 7.2 years, 21,795 patients (0.89%) were newly diagnosed with pancreatobiliary cancer. The adjusted hazard ratio for pancreatobiliary cancer in quartiles 2–4 compared with that in quartile 1 was 1.091, 1.223, and 1.554, respectively, demonstrating a significant upward trend (p<0.001). This association remained consistent across all cancer types and was independent of the DM duration or treatment regimen. Conclusions Elevated serum GGT levels were independently associated with an increased risk of pancreatobiliary cancer, regardless of the duration of DM or the use of oral hypoglycemic agents and insulin. While these findings suggest the potential utility of serum GGT as a biomarker for identifying individuals at higher risk of pancreatobiliary cancer within the diabetic population, further research is needed to validate its clinical applicability.

Background/Aims: Chronic gastroesophageal reflux disease (GERD) requires symptom relief and treatment of associated conditions. In this study, we aimed to compare anti-reflux mucosectomy (ARMS) and Stretta radiofrequency (SRF) for treating patients with chronic GERD who are unresponsive to proton pump inhibitors (PPIs) and to identify the indications for each procedure. Methods: Data of patients who underwent ARMS or SRF between March 2021 and April 2023 were analyzed. Changes in GERD questionnaire (GERDQ) scores, endoscopic Los Angeles (LA) grade, flap valve grade (FVG) based on Hill’s type, EndoFLIP distensibility index (DI), endoscopic Barrett’s epithelium (BE) resolution rate, and PPI withdrawal rate were compared between the two groups. Results: Improvements in the GERDQ scores and PPI withdrawal rates were similar between the groups. The ARMS group showed significantly better changes in endoscopic LA grade, FVG, and EndoFLIP DI than the SRF group. The complications were more prevalent in the ARMS group than in the SRF group. Conclusions The change in endoscopic LA grade before and after the procedure was significantly higher in the ARMS group than in the SRF group. Significant improvements in endoscopic FVG, BE resolution, and EndoFLIP DI were observed only with the ARMS group.

Purpose: Pancreatic cancer has a poor prognosis; however, the implementation of neoadjuvant treatment enables borderline resectable cases to undergo curative resection and improves the overall survival rate. Attempts have been made to expand the eligibility criteria for neoadjuvant treatment, even in resectable cases. Some studies have suggested a correlation between vein abutment and poor prognosis or that the abutment angle may affect prognosis. This study investigated the anatomical factors affecting the vessel abutment angle and its prognostic value in pancreatic cancer. Methods: Patients with pancreatic ductal adenocarcinoma who underwent surgery between 2012 and 2017 were included in this study. Patients who underwent neoadjuvant treatment were excluded. Data from only the intent-to-treat pancreaticoduodenectomy group were included in the analysis. Clinicopathological characteristics; preoperative factors such as CA 19-9, preoperative biliary drainage, American Society of Anesthesiologists physical status classification, portal vein/superior mesenteric vein contact angle measured via CT scan; and intraoperative factors were collected for analysis. Results: A total of 365 patients were included in this study, and the abutment group included 92 patients (25.2%). The abutment and no-contact groups did not show any significant differences in terms of the overall survival or diseasefree survival rate. Among the abutment groups, patients with less than 90° and 90°–180° did not show any significant differences. In the multivariate analysis, the only preoperative factor that had a prognostic effect was CA 19-9, a biological factor. Conclusion When there is no vessel invasion in the abutment group, upfront surgery should be considered because the angle does not affect the overall prognosis.

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.

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.

Background/Aims: Multiple myeloma (MM) predominantly affects elderly individuals, but studies on older patients with MM are limited. The clinical characteristics and survival outcomes of patients with MM aged 80 years or over were retrospectively analyzed. Methods: This retrospective multicenter study was conducted to investigate the clinical characteristics, treatment patterns, and survival outcomes of patients aged 80 years or over who were newly diagnosed with MM at five academic hospitals in Daegu, Korea, between 2010 and 2019. Results: A total of 127 patients with a median age of 83 years (range, 80–93 yr) were enrolled: 52 (40.9%) with Eastern Cooperative Oncology Group Performance Status (ECOG PS) > 2, 84 (66.1%) with International Staging System (ISS) stage III disease, and 93 (73.2%) with a Charlson comorbidity index (CCI) > 4. Chemotherapy was administered to 86 patients (67.7%). The median overall survival was 9.3 months. Overall survival was significantly associated with ECOG PS > 2 (HR 2.26, 95% CI 1.43–3.59), ISS stage III (HR 1.99, 95% CI 1.18–3.34), and chemotherapy (HR 0.34, 95% CI 0.21–0.55). There was no statistically significant difference in event-free survival according to the type of anti-myeloma chemotherapy administered. The early mortality (EM) rate was 28.3%. Conclusions Even in patients with MM aged 80 years or over, chemotherapy can result in better survival outcomes than supportive care. Patients aged ≥ 80 years should not be excluded from chemotherapy based on age alone. However, reducing EM in elderly patients with newly diagnosed MM remains challenging.

Background: We investigated whether nutritional intake is associated with physical activity (PA) and handgrip strength (HGS) in individuals with airflow limitation. Methods: This study analyzed data from the 2014 and 2016 Korean National Health and Nutrition Examination Survey. We assessed total protein intake (g/day), caloric intake (kcal/day), and other nutritional intakes, using a 24-hour dietary recall questionnaire. HGS was measured three times for each hand using a digital grip strength dynamometer, and PA was assessed as health-enhancing PA. Airflow limitation was defined as a forced expiratory volume/forced vital capacity ratio of 0.7 in individuals over 40 years of age. Participants were categorized into groups based on their PA levels and HGS measurements: active aerobic PA vs. non-active aerobic PA, and normal HGS vs. low HGS. Results: Among the 622 individuals with airflow limitation, those involved in active aerobic PA and those with higher HGS had notably higher total food, calorie, water, protein, and lipid intake. The correlations between protein and caloric intake with HGS were strong (correlation coefficients=0.344 and 0.346, respectively). The forest plots show that higher intakes of food, water, calories, protein, and lipids are positively associated with active aerobic PA, while higher intakes of these nutrients are inversely associated with low HGS. However, in the multivariate logistic regression analysis, no significant associations were observed between nutritional intake and active aerobic PA or HGS. Conclusion Nutritional intake was found to not be an independent factor associated with PA and HGS. However, the observed correlations suggest potential indirect effects that warrant further investigation.