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Purpose@#The Korea University Anam Hospital Seoul Poison Control Center (SeoulPCC) was established in accordance with Seoul Metropolitan Government Ordinance No. 7524 “Seoul Metropolitan Government Ordinance on the Prevention of Toxic Substances Poisoning and Accident Safety.” Herein, the center’s annual performance in terms of project results and consultation information for 2022 are reported. @*Methods@#SeoulPCC operates a helpline (Help Call, 1855-2221) that the general public can use from 9:00 AM to 5:00 PM on weekdays, as well as chatting and chatbot counseling through KakaoTalk’s “Seoul Poison Control Center,” and one-on-one online counseling through the website. Additionally, it has constructed a system for communicating with the general public through social media. Poisoning disease information collected from SeoulPCC from January to December 2022 was analyzed according to the number of requests, age of exposure, gender, location, and reason. Requests from the general public were summarized, and a brief image presenting information on poisoning disease-related consultations was produced. @*Results@#SeoulPCC has a database containing information on 188,065 toxic substances collected by public institutions and provides this information to the general public and medical staff through its website. In 2022, consultations were performed through phone calls and SNS (social networking service) for 577 cases of poison information and first aid treatment due to exposure to toxic substances. There were 1,431 instances of providing poison information services. The annual requests included 512 exposure cases and 65 non-exposure cases. Furthermore, 366 cases were in Seoul, 145 cases were outside of Seoul, and 66 had an unknown location. The exposure cases included 161 requests from the general public and 351 requests from medical staff. @*Conclusion@#This is the first annual report in Korea to analyze the occurrence of poisoning based on consultations. It is of major significance that this report serves as a starting point for identifying and tracking the aspects and characteristics of poisoning cases in the pre-hospital stage. In the future, poisoning-related disease information provided through consultations and at the emergency room should be linked, and through real-time collection and analysis, this information should be used as basic data for poisoning disease management policies.
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Background@#Readmissions related to lack of quality care harm both patients and health insurance finances. If the factors affecting readmission are identified, the readmission can be managed by controlling those factors. This paper aims to identify factors that affect readmissions of convalescent rehabilitation patients. @*Methods@#Health Insurance Review and Assessment Service claims data were used to identify readmissions of convalescent patients who were admitted in hospitals and long-term care hospitals nationwide in 2018. Based on prior research, the socio-demographics, clinical, medical institution, and staffing levels characteristics were included in the research model as independent variables. Readmissions for convalescent rehabilitation treatment within 30 days after discharge were analyzed using logistic regression and generalization estimation equation. @*Results@#The average readmission rate of the study subjects was 24.4%, and the risk of readmission decreases as age, length of stay, and the number of patients per physical therapist increase. In the patient group, the risk of readmission is lower in the spinal cord injury group and the musculoskeletal system group than in the brain injury group. The risk of readmission increases as the severity of patients and the number of patients per rehabilitation medicine specialist increases. Besides, the readmission risk is higher in men than women and long-term care hospitals than hospitals. @*Conclusion@#“Reducing the readmission rate” is consistent with the ultimate goal of the convalescent rehabilitation system. Thus, it is necessary to prepare a mechanism for policy management of readmission.
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PURPOSE@#The aim of this study was to investigate and compare the color stability of provisional restorative materials fabricated by 3D printing, dental milling, and conventional materials. @*MATERIALS AND METHODS@#For the experimental groups, two commercially available 3D-printing provisional resins (E-Dent 100; EnvisionTEC GmbH, Germany & VeroGlaze TM ; Stratasys® , USA), two dental milling blocks (PMMA Disk; Yamahachi Dental Co., Japan & Telio® CAD; Ivoclar Vivadent AG, Liechtenstein), and two conventional materials (AlikeTM ; GC Co., Japan & Luxatemp automix plus; DMG, Germany) were used. The water sorption and solubility test were (n=10, respectively) carried out according to ISO4049:2000 (International Standards Organization, Geneva, Switzerland). For the color stability test (n=10), coffee and black tea were used as staining solutions, and the specimens were stored for 12 weeks. Data were analyzed by one-way ANOVA and Tukey’s HSD using SPSS version 22.0 (SPSS Inc. Chicago, IL, USA) (P<.05). @*RESULTS@#Alike and Veroglaze showed the highest values and Luxatemp showed the lowest water sorption. In the color stability test, the ΔE of conventional materials varied depending on the staining solution. PMMA milling blocks showed a relatively low ΔE up to 4 weeks, and then significantly increased after 8 weeks (P<.05). 3D-printed materials exhibited a high ΔE or a significant increase over time (P<.05). @*CONCLUSION@#The degree of discoloration increased with time, and a visually perceptiblecolor difference value (ΔE) was shown regardless of the materials and solutions. PMMA milled and 3D-printed materials showed more rapid change in discoloration after 8 weeks.
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PURPOSE@#. The purpose of this in vitro study was to examine the reliability of the Anycheck device and the effect of the healing abutment diameter on the Anycheck values (implant stability test, IST). @*MATERIALS AND METHODS@#. Thirty implants were placed into three artificial bone blocks with 10 Ncm, 15 Ncm, and 35 Ncm insertion torque value (ITV), respectively (n = 10). (1) The implant stability was measured with three different kinds of devices (Periotest M, Osstell ISQ Mentor, and Anycheck). (2) Five different diameters (4.0, 4.5, 4.8, 5.5, and 6.0 mm) of healing abutments of the same height were connected to the implants and the implant stability was measured four times in different directions with Anycheck. The measured mean values were statistically analyzed. @*RESULTS@#. The correlation coefficient between the mean implant stability quotient (ISQ) and IST value was 0.981 (P<.01) and the correlation coefficient between the meant periotest value (PTV) and IST value was -0.931 (P<.01). There were no statistically significant differences among the IST values with different healing abutment diameters. @*CONCLUSION@#. There was a strong correlation between the Periotest M and Anycheck values and between the ISQ and IST. The diameter of the healing abutment had no effect on the Anycheck values.