Objectives: To evaluate and compare the clinical outcomes of cervical transforaminal epidural block (CTEB) using local anesthetics with or without a steroid for cervical spondylotic radiculopathy (CSR).Summary of Literature Review: The typical mixture for a CTEB is a combination of local anesthetics with a non-particulate steroid.However, there are potential complications related to steroid injections such as steroid-induced osteoporosis, hypothalamus-pituitaryadrenal gland axis suppression, and hyperglycemia. Materials and Methods: From January 2018 to October 2019, 35 patients who underwent CTEB for CSR were enrolled in this study.Cases with arm pain over 4 on a visual analog scale (VAS) were included. In the first 19 cases, a combination of 1 mL of 1% lidocaine and 1 mL of dexamethasone was used (group A), and in the next 16 cases, 1 mL of 1% lidocaine mixed with 1 mL of normal saline was used (group B). Arm pain VAS and the Neck Disability Index (NDI) were obtained perioperatively. Results: Baseline characteristics were not significantly different between the two groups. In both groups, the arm pain VAS significantly decreased at 30 minutes, 2 weeks, and 6 weeks post-injection compared to pre-injection values. However, the arm pain aggravated 12 weeks post-injection. The NDI of both groups significantly improved 6 weeks post-injection compared to pre-injection. The clinical outcomes of arm pain VAS and NDI at 30 minutes, 2 weeks, and 6 weeks post-injection, as well as the amounts of change, were not significantly different between both groups. Conclusions CTEB for CSR without a steroid improved symptoms by 6 weeks. The degree of improvement was similar to when CTEB was performed with a steroid in terms of VAS and NDI.

Brace treatment is a well-documented conservative treatment method for adolescent idiopathic scoliosis. On the other hand, previous studies reported skin problems, decreased lung capacity, and chest wall deformity as complications. This paper reports a case of spontaneous rib fracture during brace treatment for adolescent idiopathic scoliosis, a complication in a 14-year-old female who eventually underwent surgical correction.

PURPOSE: Fitz-Hugh-Curtis Syndrome (FHCS) is a clinical entity characterized by inflammation of the liver capsule associated with genital tract infection. The aim of this study is to provide physicians with clinical suggestions for diagnostic approaches based on a series of patients who were diagnosed with FHCS. MATERIALS AND METHODS: We conducted a retrospective study of patients who were diagnosed with FHCS after presenting to the emergency department (ED). The symptoms, physical examinations, laboratory findings, radiological findings, and progress of the patients were reviewed. RESULTS: During the four-year study period, a total of 82 female patients received a final diagnosis of FHCS in the ED. Chlamydia trachomatis was identified as a pathogen in 89% of the patients. Their clinical characteristics and laboratory findings were described. Fifty-two patients (63.4%) were admitted to the hospital. All of the admitted patients improved after treatment combining antibiotic therapy with conservative care. CONCLUSION: FHCS should be considered as a differential diagnosis for female patients of childbearing age with right upper abdominal pain. Timely diagnosis using biphasic computed tomography (CT) with arterial and portal phases may help ensure adequate medical treatment as well as avoid invasive procedures.
Adolescent ; Adult ; Anti-Bacterial Agents/therapeutic use ; Chlamydia Infections/drug therapy/microbiology/*pathology/radiography ; Chlamydia trachomatis/pathogenicity ; Emergency Service, Hospital ; Female ; Hepatitis/drug therapy/microbiology/*pathology/radiography ; Humans ; Pelvic Inflammatory Disease/drug therapy/microbiology/*pathology/radiography ; Peritonitis/drug therapy/microbiology/*pathology/radiography ; Reproductive Tract Infections/drug therapy/microbiology/pathology/radiography ; Retrospective Studies ; Tomography, X-Ray Computed ; Young Adult

In this case report, we present the radiologic and pathologic findings of atypical ductal hyperplasia (ADH) in the male breast. It is well known that a high-risk lesion such as ADH is a precursor of breast cancer in females. However, the clinical significance of these lesions in the male breast is still uncertain because male breasts mainly consist of ducts without lobule formation, unlike the female breast. To our knowledge, imaging findings of ADH in the male breast have not been reported previously, except for a few studies on the pathologic findings of these lesions. Through this paper, we would like to present the possible imaging features of this high-risk lesion in the male breast and review the related literature.

PURPOSE: Until now, there has been no standardized method for insertion of the introducer needle in ultrasound-guided internal jugular venous catheterization (IJVC). The needle insertion site and angle have been determined by the performer's experiences. In an effort to improve first attempt success rates and reduce complications, we designed a new standardized approach (called the 45degrees set square method) for determination of the site and angle of needle insertion during ultrasound-guided IJVC. This study investigated that the clinical usefulness of the novel 45degrees set square method for ultrasound-guided IJVC in the emergency department. METHODS: We conducted a prospective study in the emergency department (ED) of a tertiary teaching hospital. Forty one patients requiring central venous catheterization were enrolled in the study. They were randomized to either the conventional ultrasound guidance group or the 45degrees set square group. The primary outcome measure was success rate within three attempts and secondary measures were the number of trials, first-attempt success, puncture time, complications, and technical difficulty score. RESULTS: All 41 participants completed this study successfully. Twenty one consecutive patients were enrolled in the 45degrees set square group and 20 were enrolled in the conventional group. The 45degrees set square group (100%) was superior to the conventional group (60%) in overall success rate within three attempts (p=0.001) and the number of attempts (p<0.001). Significant differences in puncture time (p=0.004), hematoma (p=0.048), and technical difficulty score (p<0.001) were observed between the groups. CONCLUSION: The 45degrees set square method is an effective and safe method for ultrasound-guided internal jugular venous catheterization.
Catheterization ; Catheterization, Central Venous ; Catheters ; Central Venous Catheters ; Emergencies ; Hematoma ; Hospitals, Teaching ; Humans ; Needles ; Outcome Assessment (Health Care) ; Prospective Studies ; Punctures

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.