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.

Background: /Aim: Atezolizumab plus bevacizumab and lenvatinib are currently available as first-line therapy for the treatment of unresectable hepatocellular carcinoma (HCC). However, comparative efficacy studies are still limited. This study aimed to investigate the effectiveness of these treatments in HCC patients with portal vein tumor thrombosis (PVTT). Methods: We retrospectively included patients who received either atezolizumab plus bevacizumab or lenvatinib as first-line systemic therapy for HCC with PVTT. Primary endpoint was overall survival (OS), and secondary endpoints included progressionfree survival (PFS) and disease control rate (DCR) determined by response evaluation criteria in solid tumors, version 1.1. Results: A total of 52 patients were included: 30 received atezolizumab plus bevacizumab and 22 received lenvatinib. The median follow-up duration was 6.4 months (interquartile range, 3.9-9.8). The median OS was 10.8 months (95% confidence interval [CI], 5.7 to not estimated) with atezolizumab plus bevacizumab and 5.8 months (95% CI, 4.8 to not estimated) with lenvatinib (P=0.26 by log-rank test). There was no statistically significant difference in OS (adjusted hazard ratio [aHR], 0.71; 95% CI, 0.34-1.49; P=0.37). The median PFS was similar (P=0.63 by log-rank test), with 4.1 months (95% CI, 3.3-7.7) for atezolizumab plus bevacizumab and 4.3 months (95% CI, 2.6-5.8) for lenvatinib (aHR, 0.93; 95% CI, 0.51-1.69; P=0.80). HRs were similar after inverse probability treatment weighting. The DCRs were 23.3% and 18.2% in patients receiving atezolizumab plus bevacizumab and lenvatinib, respectively (P=0.74). Conclusion The effectiveness of atezolizumab plus bevacizumab and lenvatinib was comparable for the treatment of HCC with PVTT.

The scalp is the thickest skin in the body and protects the intracranial structures. The coverage of a large scalp defect is a difficult surgical procedure, the full details of which must be considered prior to the procedure, such as defect size and depth, and various factors related to the patient’s general condition. Although a free flap is the recommended surgical procedure to cover large scalp defects, it is a high-risk operation that is not appropriate for all patients. As such, other surgical options must be explored. We present the case of a patient with an ulcer on the scalp after wide excision and split-thickness skin graft for squamous cell cancer. We successfully performed a reverse temporalis muscle flap for this patient.

Naso-orbito-ethmoidal (NOE) fractures are complicated fractures of the mid-face. The treatment of NOE fractures is challenging and a comprehensive treatment strategy is required. We introduce a case of NOE fracture treated with open reduction and suspension sutures. A 28-year-old woman presented with a unilateral NOE fracture. To reduce the frontal process of the maxilla, a suspension suture was made by pulling the fragment using a double arm suture via a transcaruncular incision. The suture thread was placed in the horizontal plane. Another suspension suture on the inferior orbital rim assisted reduction procedure, and they passed through the overlying skin. The reduction alignment could be finely adjusted by tightening the transcutaneous suture threads while checking the degree of bone alignment through the subciliary incision. The two suture threads were suspended using a thermoplastic nasal splint. An additional skin incision on the medial canthal area, which would have resulted in a scar, could be avoided. Four months postoperatively, computed tomography showed an accurate and stable reduction. The patient was satisfied with her aesthetic appearance, and functional deficits were not present.

Objective: To investigate the usefulness of goal attainment scaling (GAS) in intensive stroke rehabilitation during the subacute stage. Methods: Medical records of subacute post-stroke patients who had undergone intensive rehabilitation under hospitalization, before and after the application of GAS, were collected. GAS was conducted at the initial evaluation of each patient. Specific goals were suggested by physical and occupational therapists and were determined by the responsible physiatrist. A 5-point scale was used for the GAS score after 4 weeks of rehabilitation according to the preset criteria of each goal. To evaluate the influence of GAS in rehabilitation practice, functional improvements were compared between two patient groups before (n=121) and after (n=141) GAS. To assess progress in GAS practice, the standard GAS score was calculated, and the changes were observed over a 3-year period. The standard GAS score converged to 50 points when the goal was achieved. The therapists who used GAS also completed a survey regarding its usefulness. Results: There were no statistical differences in the motor and cognitive outcomes of the patient groups before and after applying GAS scoring. Successive yearly changes in the standard GAS scores showed progressive convergence to 50 points, signaling that the patient’s goal-setting abilities improved. According to the survey, most therapists felt that GAS enhanced the quality of therapies (84.6%). Conclusion GAS improved goal-setting for the rehabilitation of subacute post-stroke patients and might have a positive effect on rehabilitation.

OBJECTIVE: To evaluate the effectiveness of intensive rehabilitation to support recovery of neurological function after brain tumor surgery and assess long-term satisfaction. METHODS: This retrospective study included patients with neurological impairment after brain tumor surgery who underwent intensive rehabilitation therapy between December 2013 and May 2017. To assess effectiveness of rehabilitation, functional outcomes (motor, cognition, and activities of daily living [ADL]) were compared between brain tumor group and a control group enrolling stroke patients who received equivalent rehabilitation during the study period. Long-term satisfaction with rehabilitation was evaluated by surveying family caregivers. RESULTS: This study included 21 patients with benign brain tumor, 14 with malignant brain tumor, and 108 with stroke. Significant and similar improvement in motor, cognition, and ADL function were noted in both the brain tumor group and the stroke group. Malignancy status did not influence the extent of functional improvement. According to medical records and surveys, 9 (69.2%) patients with malignant tumor and 2 (11.8%) with benign tumor had expired by the time of the survey. Most family caregivers confirmed that rehabilitation was effective for functional improvement (>60%), expressing overall satisfaction and stating they would recommend such therapy to patients with similar conditions (approximately 70%). CONCLUSION: Intensive rehabilitation may help promote functional improvement following brain tumor surgery regardless of malignancy compared with stroke patients. Family caregivers expressed overall satisfaction with rehabilitation at long-term follow-up. These findings support the provision of intensive rehabilitation therapy for neurologic function recovery following brain tumor surgery.
Activities of Daily Living ; Brain Neoplasms ; Brain ; Caregivers ; Cognition ; Follow-Up Studies ; Humans ; Medical Records ; Neurological Rehabilitation ; Pilot Projects ; Recovery of Function ; Rehabilitation ; Retrospective Studies ; Stroke ; Treatment Outcome

OBJECTIVE: To suggest rotation angles of fluoroscopy that can bypass the carotid sheath according to vertebral levels for cervical transforaminal epidural steroid injection (TFESI). METHODS: Patients who underwent cervical spine magnetic resonance imaging (MRI) from January 2009 to October 2017 were analyzed. In axial sections of cervical spine MRI, three angles to the vertical line (α, angle not to insult carotid sheath; β, angle for the conventional TFESI; γ, angle not to penetrate carotid artery) were measured. RESULTS: Alpha (α) angles tended to increase for upper cervical levels (53.3° in C6-7, 65.2° in C5-6, 75.3° in C4-5, 82.3° in C3-4). Beta (β) angles for conventional TFESI showed a constant value of 45° to 47° (47.5° in C6-7, 47.4° in C5-6, 45.7° in C4-5, 45.0° in C3-4). Gamma (γ) angles increased at higher cervical levels as did α angles (25.2° in C6-7, 33.6° in C5-6, 43.0° in C4-5, 56.2° in C3-4). CONCLUSION: The risk of causing injury by penetrating major vessels in the carotid sheath tends to increase at upper cervical levels. Therefore, prior to cervical TFESI, measuring the angle is necessary to avoid carotid vessels in the axial section of CT or MRI, thus contributing to a safer procedure.
Carotid Arteries ; Fluoroscopy ; Humans ; Jugular Veins ; Magnetic Resonance Imaging ; Needles* ; Spine ; Vascular System Injuries