Sarcopenia is a major geriatric syndrome characterized by progressive loss of muscle mass and strength, resulting in disability and mortality. This narrative review synthesizes international consensus recommendations and Korean evidence to guide context-specific sarcopenia management strategies. PubMed, Embase, Cochrane Library, and KoreaMed (January 2000–November 2025) were searched, focusing on randomized trials, meta-analyses, systematic reviews, clinical practice guidelines, and large observational studies. Global diagnostic frameworks have evolved from muscle mass-based definitions toward multidimensional models that incorporate muscle strength and physical performance. Exercise and nutrition remain the mainstay treatments, with resistance-based training and adequate protein intake. Currently, pharmacologic options with proven clinical benefit are limited. In Korea, growing evidence supports the effectiveness of community-based sarcopenia interventions, underscoring the need for standardized, integrated delivery models that bridge the fragmented healthcare system and enable sustainable implementation.

Purpose: This study aimed to develop and evaluate the effectiveness of the Trauma-nursing Education and Skill Support (TESS) program based on the ADDIE model (Analysis, Design, Development, Implementation, Evaluation model). The program was designed to enhance trauma nurses’ clinical competencies, including trauma-related knowledge, self-efficacy, and problem-solving ability, through the integration of theoretical education and simulation-based practice. Methods: A quasi-experimental study using a non-equivalent control group pretest–posttest design was conducted. Participants included 108 trauma nurses from regional trauma centers, military trauma centers, and emergency care facilities, who were assigned to an experimental group (n=52) or a control group (n=56). The TESS program consisted of a 2-day, 14-hour blended-learning course that included eight lecture sessions and four simulation-based practice stations. Data were collected at baseline, immediately after the intervention, and at 6 months using validated instruments measuring trauma-related knowledge, self-efficacy, and problem-solving ability. Two-way repeated-measures analysis of variance was used for data analysis. Results: The experimental group demonstrated significant improvements in trauma-related knowledge, self-efficacy, and problem-solving ability compared with baseline (all p<.001). These improvements were sustained at 6 months, although trauma-related knowledge scores showed a slight decline compared with immediate posttest levels. Between-group analyses confirmed significant group-by-time interaction effects for all outcomes: trauma-related knowledge (η2=0.12, p<.001), self-efficacy (η2=0.09, p=.002), and problem-solving ability (η2=0.08, p=.003). Conclusion The TESS program effectively enhanced trauma nurses’ trauma-related knowledge, self-efficacy, and problem-solving ability, with effects sustained for up to 6 months. Incorporating blended learning and simulation-based training into standardized trauma nursing education may strengthen clinical competencies and ultimately contribute to improved patient outcomes.

Background: This study aimed to develop standard job categories for constructing a job-exposure matrix (JEM) for police officers in South Korea and to evaluate their applicability. Methods: We examined standard job codes related to police personnel management and compared them with job classifications from police publications. Using R Shiny, we developed a web-based search tool for standard codes. A pilot survey of 130 police officers assessed the codes' applicability and relevance to health-related hazardous factors. Results: Eighty-seven standard functional codes used in the police personnel management system POOL were organized into minor categories as the basic units of standard jobs. These were grouped into 20 sub-major categories and further consolidated into 10 major categories to develop the standard job codes. The responses to the standard job codes in the pilot survey were 75% accurate compared with the final expert evaluation results and 99.2% accurate compared with the algorithm-based automatic allocation results. The results of the job-hazardous factor network analysis revealed that the most frequently reported hazardous factor was emotional labor, followed by night shifts and electromagnetic waves. Emotional labor was identified as the top hazardous factor in six out of the nine standard job categories. Conclusions The standard job codes developed in this study were designed in connection with the personnel management system for police officers, making them well-suited for constructing a comprehensive JEM for the entire police force.

Background: and Purpose This study evaluated the diagnostic utility of an anti-signal-recognition particle 54 (anti-SRP54) antibody-based enzyme-linked immunosorbent assay (ELISA) as well as the clinical, serological, and pathological characteristics of patients with SRP immune-mediated necrotizing myopathy (IMNM). Methods: We evaluated 87 patients with idiopathic inflammatory myopathy and 107 healthy participants between January 2002 and December 2023. The sensitivity and specificity of the ELISA for anti-SRP54 antibodies were assessed, and the clinical profiles of patients with antiSRP54 antibodies were determined. Results: The ELISA for anti-SRP54 antibodies had a sensitivity and specificity of 88% and 99%, respectively, along with a test–retest reliability of 0.92 (p<0.001). The 32 patients diagnosed with anti-SRP IMNM using a line-blot immunoassay included 28 (88%) who tested positive for anti-SRP54 antibodies using the ELISA, comprising 12 (43%) males and 16 (57%) females whose median ages at symptom onset and diagnosis were 43.0 years and 43.5 years, respectively. Symptoms included proximal muscle weakness in all 28 (100%) patients, neck weakness in 9 (32%), myalgia in 15 (54%), dysphagia in 5 (18%), dyspnea in 4 (14%), dysarthria in 2 (7%), interstitial lung disease in 2 (7%), and myocarditis in 2 (7%). The median serum creatine kinase (CK) level was 7,261 U/L (interquartile range: 5,086–10,007 U/L), and the median anti-SRP54 antibody level was 2.0 U/mL (interquartile range: 1.0–5.6 U/mL). The serum CK level was significantly higher in patients with coexisting anti-Ro-52 antibodies. Conclusions This study has confirmed the reliability of the ELISA for anti-SRP54 antibodies and provided insights into the clinical, serological, and pathological characteristics of South Korean patients with anti-SRP IMNM.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: and Purpose This study evaluated the diagnostic utility of an anti-signal-recognition particle 54 (anti-SRP54) antibody-based enzyme-linked immunosorbent assay (ELISA) as well as the clinical, serological, and pathological characteristics of patients with SRP immune-mediated necrotizing myopathy (IMNM). Methods: We evaluated 87 patients with idiopathic inflammatory myopathy and 107 healthy participants between January 2002 and December 2023. The sensitivity and specificity of the ELISA for anti-SRP54 antibodies were assessed, and the clinical profiles of patients with antiSRP54 antibodies were determined. Results: The ELISA for anti-SRP54 antibodies had a sensitivity and specificity of 88% and 99%, respectively, along with a test–retest reliability of 0.92 (p<0.001). The 32 patients diagnosed with anti-SRP IMNM using a line-blot immunoassay included 28 (88%) who tested positive for anti-SRP54 antibodies using the ELISA, comprising 12 (43%) males and 16 (57%) females whose median ages at symptom onset and diagnosis were 43.0 years and 43.5 years, respectively. Symptoms included proximal muscle weakness in all 28 (100%) patients, neck weakness in 9 (32%), myalgia in 15 (54%), dysphagia in 5 (18%), dyspnea in 4 (14%), dysarthria in 2 (7%), interstitial lung disease in 2 (7%), and myocarditis in 2 (7%). The median serum creatine kinase (CK) level was 7,261 U/L (interquartile range: 5,086–10,007 U/L), and the median anti-SRP54 antibody level was 2.0 U/mL (interquartile range: 1.0–5.6 U/mL). The serum CK level was significantly higher in patients with coexisting anti-Ro-52 antibodies. Conclusions This study has confirmed the reliability of the ELISA for anti-SRP54 antibodies and provided insights into the clinical, serological, and pathological characteristics of South Korean patients with anti-SRP IMNM.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Background/Aims: To determine the effectiveness of tyrosine kinase inhibitor (TKI) plus reduced-intensity therapy in adult patients with newly diagnosed Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph-positive ALL), this retrospective study compared treatment outcomes and induction mortality according to backbone regimen intensity. Methods: The data of 132 patients diagnosed with Ph-positive ALL were retrospectively collected from five centers. Patients received imatinib plus intensive chemotherapy (modified VPD, KALLA1407, or hyper-CVAD) or reduced-intensity chemotherapy (EWALL) for curative purposes. This study analyzed 117 patients, of which 35,22,46, and 14 received modified VPD, KALLA1407, hyper-CVAD, and EWALL, respectively. All patients used imatinib as a TKI. Results: The median age of the patients who received reduced-intensity chemotherapy was 64.4 years, while that of the patients with intensive regimens was 47.5 years. There was no induction death in the reduced-intensity group, while nine patients died in the intensive therapy group. Major molecular response achievement tended to be higher in the intensive chemotherapy group than in the reduced-intensity group. More patients in the intensive chemotherapy group received allogeneic stem cell transplantation (allo-SCT). There was no statistically significant difference in long-term survival between the two groups in terms of relapse-free survival and overall survival rates. Conclusions When imatinib plus reduced-intensity therapy was used as a frontline treatment, there was no inferiority in obtaining complete remission compared to imatinib plus intensive chemotherapy or significant difference in long-term survival. Since imatinib plus reduced-intensity therapy has limitations in obtaining a deep molecular response, proceeding to allo-SCT should be considered.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.