Purpose: This study aimed to compare the acute effects of isometric handgrip (IHG) and wall squat (IWS) exercise on 24-hour ambulatory blood pressure (BP) and related indices in young men with prehypertension. Methods: Ten young men with prehypertension (aged 25.0±2.3 years) completed three randomized crossover conditions: IHG, IWS, and control. Twenty-four-hour ambulatory BP monitoring was used to assess 24-hour, daytime, and nighttime mean BP, BP variability (BPV; average real variability and standard deviation), morning BP, and nocturnal dipping pattern. Office BP was measured at baseline, 30 minutes, and 90 minutes after each condition. Results: Twenty-four hour, daytime, and nighttime systolic and diastolic BP did not differ significantly among conditions. BPV indices also showed no significant between-condition differences across 24-hour, daytime, or nighttime periods. Nocturnal systolic BP dipping averaged 5%–8% and did not differ among conditions, indicating a nondipper pattern in all trials. Morning systolic BP, sleep-trough surge, and prewaking surge were similar across conditions. Office BP showed no significant condition or interaction effects; however, both IHG and IWS tended to lower BP at 90 minutes compared with control session. Conclusion A single bout of IHG or IWS did not elicit significant changes in 24-hour ambulatory BP, BPV, morning BP, or dipping pattern in young men with prehypertension. Nonetheless, the small acute reductions in office and morning BP suggest that isometric exercise may have potential as a time-efficient adjunctive strategy for early BP management. Larger and longer-term studies are needed to confirm these findings.

Purpose: Inspiratory muscle strength training is a time-efficient strategy for lowering blood pressure, but the acute vascular effects and its hemodynamic characteristics remain unclear. This study aimed to determine the acute effects of inspiratory muscle strength exercise (IMSE) on vascular function and shear patterns. Methods: In a single-arm acute trial, 12 young adults (aged 26±2 years) performed a single bout of IMSE (about 8 minutes) consisting of 30 breaths (6 breaths×5 sets) at 60% of maximal inspiratory pressure. Vascular function indices were assessed as endothelial function and arterial stiffness. Measurements of endothelial function via flow-mediated dilation (FMD) in the brachial artery and arterial stiffness by carotid-femoral pulse wave velocity (cfPWV) were taken at baseline, 10 minutes, and 40 minutes post-IMSE. Shear rate (SR) responses were continuously obtained throughout the IMSE session at the brachial artery and were analyzed as mean SR, anterograde SR, and retrograde SR. Results: Compared to baseline, cfPWV significantly decreased (7.95±0.9 m/sec to 7.49±0.9 m/sec, p=0.033) and FMD increased (4.76%±1.0% to 6.26%±1.1%, p＜ 0.001) at post-10 min. Both cfPWV and FMD returned to baseline levels at post-40 min. During IMSE, mean SR and anterograde SR significantly increased (both, p＜ 0.05), whereas retrograde SR decreased (p=0.032) during inter-set rest periods. In addition, rhythmic fluctuations in SR were observed corresponding to inspiration and expiration phases. Conclusion These findings suggest that an acute bout of IMSE is associated with improved endothelial function and arterial stiffness. IMSE may elicit distinct hemodynamic responses during exercise, which may be linked to improvements in vascular function.

Background: s/Aims: Reported incidence of extrahepatic bile duct cancer is higher in Asians than in Western populations. Korea, in particular, is one of the countries with the highest incidence rates of extrahepatic bile duct cancer in the world. Although research and innovative therapeutic modalities for extrahepatic bile duct cancer are emerging, clinical guidelines are currently unavailable in Korea. The Korean Society of Hepato-Biliary-Pancreatic Surgery in collaboration with related societies (Korean Pancreatic and Biliary Surgery Society, Korean Society of Abdominal Radiology, Korean Society of Medical Oncology, Korean Society of Radiation Oncology, Korean Society of Pathologists, and Korean Society of Nuclear Medicine) decided to establish clinical guideline for extrahepatic bile duct cancer in June 2021. Methods: Contents of the guidelines were developed through subgroup meetings for each key question and a preliminary draft was finalized through a Clinical Guidelines Committee workshop. Results: In November 2021, the finalized draft was presented for public scrutiny during a formal hearing. Conclusions The extrahepatic guideline committee believed that this guideline could be helpful in the treatment of patients.

The Korean Breast Cancer Society (KBCS) has collected nationwide registry data on clinicopathologic characteristics and treatment since 1996. This study aimed to analyze the clinical characteristics of breast cancer in Korea and assess changes in breast cancer statistics for 2021 using data from the KBCS registry and the Korean Central Cancer Registry. In 2021, 34,628 women were newly diagnosed with breast cancer. The median age of women diagnosed with breast cancer was 53.4 years, with the highest incidence occurring in the 40–49 age group. The most common molecular subtype was hormone receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative, accounting for 69.1% of cases, while HER2-positive subtypes comprised 19.3%. During the coronavirus disease 2019 pandemic, the national breast cancer screening rate declined. However, the incidence of early-stage breast cancer (stages 0 and I) continued to increase, accounting for 65.6% of newly diagnosed cases in 2021. Our results showed that the overall survival rate for patients with breast cancer has improved, primarily due to a rise in early-stage diagnoses and advancements in treatment.

Background: Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive.This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes. Methods: We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores. Results: The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort’s test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889–0.909) for injury mechanism, 0.774 (0.760–0.789) for place of occurrence, 0.679 (0.665–0.694) for activity, 0.972 (0.967–0.977) for intent, and 0.935 (0.926–0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy. Conclusion Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

Background: Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive.This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes. Methods: We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores. Results: The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort’s test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889–0.909) for injury mechanism, 0.774 (0.760–0.789) for place of occurrence, 0.679 (0.665–0.694) for activity, 0.972 (0.967–0.977) for intent, and 0.935 (0.926–0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy. Conclusion Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

Background: Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive.This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes. Methods: We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores. Results: The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort’s test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889–0.909) for injury mechanism, 0.774 (0.760–0.789) for place of occurrence, 0.679 (0.665–0.694) for activity, 0.972 (0.967–0.977) for intent, and 0.935 (0.926–0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy. Conclusion Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

The Korean Breast Cancer Society (KBCS) has collected nationwide registry data on clinicopathologic characteristics and treatment since 1996. This study aimed to analyze the clinical characteristics of breast cancer in Korea and assess changes in breast cancer statistics for 2021 using data from the KBCS registry and the Korean Central Cancer Registry. In 2021, 34,628 women were newly diagnosed with breast cancer. The median age of women diagnosed with breast cancer was 53.4 years, with the highest incidence occurring in the 40–49 age group. The most common molecular subtype was hormone receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative, accounting for 69.1% of cases, while HER2-positive subtypes comprised 19.3%. During the coronavirus disease 2019 pandemic, the national breast cancer screening rate declined. However, the incidence of early-stage breast cancer (stages 0 and I) continued to increase, accounting for 65.6% of newly diagnosed cases in 2021. Our results showed that the overall survival rate for patients with breast cancer has improved, primarily due to a rise in early-stage diagnoses and advancements in treatment.

The Korean Breast Cancer Society (KBCS) has collected nationwide registry data on clinicopathologic characteristics and treatment since 1996. This study aimed to analyze the clinical characteristics of breast cancer in Korea and assess changes in breast cancer statistics for 2021 using data from the KBCS registry and the Korean Central Cancer Registry. In 2021, 34,628 women were newly diagnosed with breast cancer. The median age of women diagnosed with breast cancer was 53.4 years, with the highest incidence occurring in the 40–49 age group. The most common molecular subtype was hormone receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative, accounting for 69.1% of cases, while HER2-positive subtypes comprised 19.3%. During the coronavirus disease 2019 pandemic, the national breast cancer screening rate declined. However, the incidence of early-stage breast cancer (stages 0 and I) continued to increase, accounting for 65.6% of newly diagnosed cases in 2021. Our results showed that the overall survival rate for patients with breast cancer has improved, primarily due to a rise in early-stage diagnoses and advancements in treatment.

Background: Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive.This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes. Methods: We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores. Results: The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort’s test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889–0.909) for injury mechanism, 0.774 (0.760–0.789) for place of occurrence, 0.679 (0.665–0.694) for activity, 0.972 (0.967–0.977) for intent, and 0.935 (0.926–0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy. Conclusion Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.