Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Background and Objectives: Lipid lowering therapy is essential to reduce the risk of major cardiovascular events; however, limited evidence exists regarding the use of statin with ezetimibe as primary prevention strategy for middle-aged adults. We aimed to investigate the impact of single pill combination therapy on clinical outcomes in relatively healthy middleaged patients when compared with statin monotherapy. Methods: Using the Korean National Health Insurance Service database, a propensity score match analysis was performed for baseline characteristics of 92,156 patients categorized into combination therapy (n=46,078) and statin monotherapy (n=46,078) groups. Primary outcome was composite outcomes, including death, coronary artery disease, and ischemic stroke. And secondary outcome was all-cause death. The mean follow-up duration was 2.9±0.3 years. Results: The 3-year composite outcomes of all-cause death, coronary artery disease, and ischemic stroke demonstrated no significant difference between the 2 groups (10.3% vs.10.1%; hazard ratio [HR], 1.022; 95% confidence interval [CI], 0.980–1.064; p=0.309).Meanwhile, the 3-year all-cause death rate was lower in the combination therapy group than in the statin monotherapy group (0.2% vs. 0.4%; p<0.001), with a significant HR of 0.595 (95% CI, 0.460–0.769; p<0.001). Single pill combination therapy exhibited consistently lower mortality rates across various subgroups. Conclusions Compared to the statin monotherapy, the combination therapy for primary prevention showed no difference in composite outcomes but may reduce mortality risk in relatively healthy middle-aged patients. However, since the study was observational, further randomized clinical trials are needed to confirm these findings.

Primary cutaneous mucinous carcinoma (PCMC) is a rare malignant neoplasm that originates from the deepest part of the eccrine sweat glands. Characterized by slow growth, PCMC often appears on the head and neck of older patients. Although it rarely metastasizes, its high recurrence rate leads to significant morbidity. Clinically differentiating PCMC from benign tumors is challenging due to its slow growth and asymptomatic nature, and a biopsy is often required for a definitive diagnosis. A 77-year-old man presented with a non-tender lesion on his left lower eyelid that had been gradually progressing over a decade. The lesion was excised, and histological examination confirmed it as PCMC. A positron emission tomography-computed tomography scan conducted to differentiate between primary and metastatic cancer showed no abnormal findings. Computed tomography was then performed to assess the remaining primary site, followed by a wide excision. The frozen biopsy revealed no cancer in the margins from five directions; however, the permanent biopsy confirmed the presence of cancer in the base margin. After consultation with the hematology-oncology department, the patient underwent additional radiation therapy. One year post-surgery, there were no signs of recurrence.

Background and Objectives: Lipid lowering therapy is essential to reduce the risk of major cardiovascular events; however, limited evidence exists regarding the use of statin with ezetimibe as primary prevention strategy for middle-aged adults. We aimed to investigate the impact of single pill combination therapy on clinical outcomes in relatively healthy middleaged patients when compared with statin monotherapy. Methods: Using the Korean National Health Insurance Service database, a propensity score match analysis was performed for baseline characteristics of 92,156 patients categorized into combination therapy (n=46,078) and statin monotherapy (n=46,078) groups. Primary outcome was composite outcomes, including death, coronary artery disease, and ischemic stroke. And secondary outcome was all-cause death. The mean follow-up duration was 2.9±0.3 years. Results: The 3-year composite outcomes of all-cause death, coronary artery disease, and ischemic stroke demonstrated no significant difference between the 2 groups (10.3% vs.10.1%; hazard ratio [HR], 1.022; 95% confidence interval [CI], 0.980–1.064; p=0.309).Meanwhile, the 3-year all-cause death rate was lower in the combination therapy group than in the statin monotherapy group (0.2% vs. 0.4%; p<0.001), with a significant HR of 0.595 (95% CI, 0.460–0.769; p<0.001). Single pill combination therapy exhibited consistently lower mortality rates across various subgroups. Conclusions Compared to the statin monotherapy, the combination therapy for primary prevention showed no difference in composite outcomes but may reduce mortality risk in relatively healthy middle-aged patients. However, since the study was observational, further randomized clinical trials are needed to confirm these findings.

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.

Primary cutaneous mucinous carcinoma (PCMC) is a rare malignant neoplasm that originates from the deepest part of the eccrine sweat glands. Characterized by slow growth, PCMC often appears on the head and neck of older patients. Although it rarely metastasizes, its high recurrence rate leads to significant morbidity. Clinically differentiating PCMC from benign tumors is challenging due to its slow growth and asymptomatic nature, and a biopsy is often required for a definitive diagnosis. A 77-year-old man presented with a non-tender lesion on his left lower eyelid that had been gradually progressing over a decade. The lesion was excised, and histological examination confirmed it as PCMC. A positron emission tomography-computed tomography scan conducted to differentiate between primary and metastatic cancer showed no abnormal findings. Computed tomography was then performed to assess the remaining primary site, followed by a wide excision. The frozen biopsy revealed no cancer in the margins from five directions; however, the permanent biopsy confirmed the presence of cancer in the base margin. After consultation with the hematology-oncology department, the patient underwent additional radiation therapy. One year post-surgery, there were no signs of recurrence.

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.