OBJECTIVES: Schizophrenia patients have deficits of prediction and learning related to dopaminergic dysfunction. It is hypothesized that there would be different characteristics in associative learning of reward approach and loss aversion between controls and patients. METHODS: Participants were 23 healthy participants and 20 out-patients fulfilling criteria for schizophrenia according DSM-IV-TR. Using a monetary incentive contingency reversal task, successful learning rates, numbers of trials and errors till learning, numbers of trials of maintaining learning, response times were measured. Characteristics of learning were compared between controls and patients. RESULTS: Physical anhedonia and PANSS negative symptom scores correlated with the number of trials while loss aversion was maintained. Overall correct response rates were decreased in patient group, particularly during reward approach learning. Patients required more trials and errors to learn reward approach than controls. There were no significant differences in learning performance and reaction times between groups during loss avoidance learning. CONCLUSION: These results support previous reports of deficits in reward-driven learning in schizophrenia. However, anhedonia and negative symptoms were associated with the preserved function of loss avoidance learning.
Anhedonia ; Avoidance Learning ; Humans ; Learning ; Motivation ; Outpatients ; Reaction Time ; Reinforcement (Psychology) ; Reward ; Schizophrenia

OBJECTIVE: We aimed to investigate the influence of area-level factors on adolescent suicide and to determine which variables differ according to age and sex.METHODS: We selected variables that were available for collection through an online database from 2005 to 2015 in the Korean Statistical Information Service and the Korea Labor Institute. We used administrative districts of Korea in 2017 for geographical classification. We examined the relationships between regional suicide rates and area-level variables in male and female subjects aged 10–14 years and 15–19 years. In addition, we analyzed area-level variables in adolescents aged 15–19 years according to sex.RESULTS: Our findings indicated that several area-level variables affected adolescent suicide rates, varying according to age and sex. Economic problems were shown to be more associated with suicide in male adolescents than in female adolescents. On the other hand, social fragmentation and health services were shown to be more associated with suicide in females.CONCLUSION: Suicide in adolescents was attributable to area-level factors such as economic status, social fragmentation, and community health services. By identifying area-level variables affecting adolescent suicide rates, we will be able to contribute to implement mental health policies related to adolescent suicide.
Adolescent ; Classification ; Community Health Services ; Female ; Hand ; Health Services ; Humans ; Information Services ; Korea ; Male ; Mental Health ; Suicide

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

BACKGROUND: The purpose of this study was to identify the characteristics of injuries of school-aged children transported via emergency medical services (EMS) that occurred in schools by comparing with injuries that occurred outside of school. METHODS: Data from the 119 EMS from 2012 to 2014 were analyzed. School and non-school injuries were analyzed in children 6 to 17 years of age. The epidemiologic characteristics were assessed according to school-age groups; low-grade primary (6–8 years), high-grade primary (9–13 years), middle (13–15 years) and high (15–17 years) school. Gender-stratified multivariable logistic regression analysis was conducted to estimate the risks of school injury in each age group. RESULTS: During the study period, a total of 167,104 children with injury were transported via 119 ambulances. Of these injuries, 13.3% occurred at schools. Boys accounted for 76.9% of school injuries and middle school children accounted for a significantly greater proportion (39.6%) of school injuries (P < 0.001). The most frequent mechanisms of injury at school were falls (43.8%). The peak times for school injury occurrence were lunch time (13:00–13:59) in all age groups. Multivariate regression identified the risky age groups as high-grade primary (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.09–1.20) and middle school-aged boys (OR, 1.82; 95% CI, 1.74–1.90) and middle school-aged girls (OR, 1.30; 95% CI, 1.21–1.40). CONCLUSION: Notable epidemiologic differences exist between in- and out-of-school injuries. The age groups at risk for school injuries differ by gender.
Accidental Falls ; Ambulances ; Child ; Emergencies* ; Emergency Medical Services* ; Epidemiology ; Female ; Humans ; Korea* ; Logistic Models ; Lunch

Background: Adverse lipid profiles are observed in postmenopausal women. However, there is insufficient evidence of the association between lipids and reproductive aging in Korean women. We aimed to characterize lipid changes with respect to timing relative to menopause in Korean middle-aged women. Methods: This study included 1,436 premenopausal women who had a natural menopause during the follow-up period (median = 15.76 years) from the Korean Genome and Epidemiology Study (KoGES) Ansan and Anseong cohort. Lipid levels were measured every 2 years, and the magnitudes of annual lipid changes and differences in the changes by premenopausal body mass index were estimated using piecewise linear mixed-effects models. Results: All lipid levels increased greatly from 3 or 5 years before menopause to 1 year after menopause in all women, regardless of their premenopausal body mass index. During the period, high-density lipoprotein cholesterol (HDL-C) levels increased at 0.42 mg/dL per year (95% confidence interval [CI], 0.29 to 0.55 mg/dL). Nevertheless, non-HDL-C levels simultaneously increased at 3.42 mg/dL per year (95% CI, 3.11 to 3.72 mg/dL), and an annual change in the non-HDL-C to HDL-C ratio was 0.05 (95% CI, 0.04 to 0.06). One year after menopause, changes in all lipid parameters significantly slowed down, except for the nonHDL-C to HDL-C ratio (P < 0.001 for all). The ratio continued to increase until 3 years after menopause, but thereafter, the change leveled off. Conclusion Women experienced remarkable increases in lipid levels during menopausal transition, highlighting the need for early intervention strategies for cardiovascular disease prevention in women.

Background: Depression is a global mental health concern that negatively affects individuals’ health and increases medical costs. This study aimed to assess whether early depression management is cost-beneficial and effective from the perspective of quality indicators. Methods: Data of patients newly diagnosed with depressive disorder between 2012 and 2014 as well as follow-up data until 2020 were extracted from the National Health Insurance Service database. Hospitalization, emergency room visits, and annual medical expenses were set as dependent variables to estimate the effect of depression and information on medical expenditures. Six quality indicators developed by the Health Insurance Review and Assessment Service comprised independent variables. Results: In total, 465,766 patients were included in this study. Patients who met the quality indicators were more likely to be hospitalized with a psychiatric diagnosis. Furthermore, patients who met the quality indicator of revisiting within 3 weeks of their first visit had greater psychiatric and overall expenses during the early treatment phase; however, the overall expenses gradually decreased over time. Conclusion High-quality initial treatment for depression can be cost-effective in the long term; however, further studies are needed to discern its immediate clinical effects.

Background/Aims: With increased life expectancy, the management of elderly hepatocellular carcinoma (HCC) patients became a crucial issue, yet it is still challenging due to comorbidities and high surgical risks. While surgical resection is considered as primary treatment for eligible HCC patients, systematic evidence on its outcomes in elderly patients remains scarce. In this review, we aimed to analyze the efficacy and safety outcomes of surgical resection in elderly HCC patients. Methods: The studies included in this meta-analysis were selected from Ovid-MEDLINE, OvidEmbase, CENTRAL, KoreaMed, KMbase, and KISS databases following a predefined protocol.Efficacy outcomes included overall survival and disease-free survival, while the safety outcomes included postoperative mortality and complications. Results: Patients in the elderly group (≥65 years) who underwent surgery exhibited non-inferior overall survival (hazard ratio [HR], 1.26; 95% confidence interval [CI], 0.92 to 1.74) and diseasefree survival (HR, 1.03; 95% CI, 0.99 to 1.08) compared to the non-elderly group. Overall postop-erative mortality exhibited no statistical difference (odds ratio [OR], 1.07; 95% CI, 0.87 to 1.31), but 30-day, 90-day, and in-hospital mortality were higher in the elderly group. The incidence of overall complications was higher in the elderly group (OR, 1.44; 95% CI, 1.22 to 1.69). Sensitivity analysis for the super elderly group (≥80 years) showed significantly higher in-hospital mortality compared to the non-super elderly group (OR, 2.51; 95% CI, 1.16 to 5.45). Conclusions The efficacy outcome of surgical resection in the elderly HCC patients was not worse than that in the non-elderly HCC patients, while in-hospital mortality and complications rates were higher. Therefore, surgical resection should be purposefully considered in the elderly population, with careful candidate selection.