In this study, we aimed to manufacture a patient-specific gel phantom combining threedimensional (3D) printing and polymer gel and evaluate the radiation dose and dose profile using gel dosimetry. Methods:The patient-specific head phantom was manufactured based on the patient's computed tomography (CT) scan data to create an anatomically replicated phantom; this was then produced using a ColorJet 3D printer. A 3D polymer gel dosimeter called RTgel-100 is contained inside the 3D printing head phantom, and irradiation was performed using a 6 MV LINAC (Varian Clinac) X-ray beam, a linear accelerator for treatment. The irradiated phantom was scanned using magnetic resonance imaging (Siemens) with a magnetic field of 3 Tesla (3T) of the Korea Institute of Nuclear Medicine, and then compared the irradiated head phantom with the dose calculated by the patient's treatment planning system (TPS). Results:The comparison between the Hounsfield unit (HU) values of the CT image of the patient and those of the phantom revealed that they were almost similar. The electron density value of the patient's bone and brain was 996±167 HU and 58±15 HU, respectively, and that of the head phantom bone and brain material was 986±25 HU and 45±17 HU, respectively. The comparison of the data of TPS and 3D gel revealed that the difference in gamma index was 2%/2 mm and the passing rate was within 95%.Conclusions: 3D printing allows us to manufacture variable density phantoms for patient-specific dosimetric quality assurance (DQA), develop a customized body phantom of the patient in the future, and perform a patient-specific dosimetry with film, ion chamber, gel, and so on.

BACKGROUND Securing the representativeness of the study population is crucial in biomedical research because it can increase the generalizability of a study. In this respect, using multi-institutional data has great advantages in medicine. However, it is difficult to combine data physically because the confidential nature of biomedical data causes privacy issues. Therefore, to use multi-institution medical data for research, a methodological approach is needed to build a model without sharing data between institutions. OBJECTIVE The objective of our study is to build an integrated predictive model of multi-institutional data, which is not require iterative communication between institutions, to increase generalizability of the model under privacy-preserving without sharing patient-level data. METHODS The weight-based integrated model (WIM) generates a weight for each institutional model and builds an integrated model for multi-institutional data based on the weight. We performed two simulations to show weight’s characteristics and to decide the number of repetitions of the weight for obtaining stable the weight. And, we conducted an experiment using real multi-institutional data to verify the developed WIM. It selected 10 hospitals (a total of 2,845 ICU stays) from the eICU Collaborative Research Database for predicting ICU mortality with 11 features. To evaluate validity of our model compared to centralized model, which was built by combining all the data of 10 hospitals, we used proportional overlap (0.5 or less indicates a significant difference at a significance level of 0.05; 2 indicates two CIs overlapping completely). Standard and firth logistic regression models were applied for two simulations and the experiment. RESULTS As results of simulations, we showed that the weight of each institution is determined by two factors, the data size of each institution and how well each institutional model fits into the overall institutional data, and that it is necessary to repeatedly generate 200 weights per institution. In the experiment, the estimated AUC and 95% CIs were 81.36% (79.37–83.36%) and 81.95% (80.03–83.87%) in the centralized model and WIM, respectively. The proportion of overlap of the CIs for AUC in both WIM and the centralized model was approximately 1.70. The proportion of overlap of the 11 estimated ORs was over 1, except for one case. CONCLUSIONS In the experiment using real multi-institutional data, our model showed the similar results as the centralized model without iterative communication between institutions. Also, WIM provided a weighted average model by integrating 10 models overfitted or underfitted compared to the centralized model. WIM will provide an efficient distributed research algorithm in that it increases the generalizability of the model and does not iterative communication.

COVID-19 has officially been declared a pandemic. The spread of the virus is placing extraordinary and sustained demands on public health systems. There is speculation that the differences in mortality rates between regions are a result of an abundance and availability of medical resources. The selection of patients for diagnosis and treatment is essential in a situation where medical resources are scarce. Military personnel are especially at risk of such an infectious disease, and patient selection with an evidence-based prognostic model is critical.

BACKGROUND Multiple vaccinations have been practiced widely in children to reduce hospital visits and the number of mental distresses ahead of vaccinations. However, in practice, how the fever patterns differ between multiple vaccinations and single vaccination is unknown. OBJECTIVE We investigated the postvaccination fever patterns of six vaccines—diphtheria/tetanus/pertussis, pneumococcus, Haemophilus influenzae B, polio, rotavirus, and influenza—recommended for children aged 2-6 months in the National Immunization Program of Korea by collecting logs of registered users of a mobile application. METHODS Multiple vaccinations were defined as simultaneous administration of ≥2 of the six vaccines. Further, multiple vaccination cases were divided into vaccination with (multiple-in) and without (multiple-out) the vaccine of interest. Postvaccination fever (≥38.0 °C) for each vaccine within 72h after vaccination was compared among single, multiple-in, and multiple-out groups according to antipyretic use. RESULTS In DTaP with antipyretics, single vaccination had similar the duration time (hour) of postvaccination fever to multiple-in, however multiple-in lasted about an hour longer than multiple-out. In PCV with and without antipyretics, single vaccination had similar the onset time when the first fever (≥38.0℃) occurred after vaccination to multiple-in, however the onset time of multiple-in was 5 to 7 hours faster than multiple-out. CONCLUSIONS Multiple vaccinations did not induce more severe fever or have rapid onset compared with a single vaccination. The post-vaccination fever patterns shown in single vaccination also appeared in multiple vaccinations including the vaccine. CLINICALTRIAL Not indicated

BACKGROUND Deliberate self-harm (DSH) is a well-known contributor to suicide-related deaths. South Korea has the highest suicide death rate in recent years among all Organization for Economic Co-operation and Development countries. OBJECTIVE We aimed to address the following questions: 1) are there significant differences in demographics, socioeconomic status, and clinical features in individuals who received psychiatric diagnosis and those who did not? 2) does receiving a psychiatric diagnosis from the department of psychiatry as opposed to other departments affect survival? and 3) which factors related to DSH contribute to deaths by suicide? METHODS We used the Korean National Health Insurance Service Database to design a cohort of 5640 individuals (54·4% women) who were admitted to the hospital between 2002 and 2020, after DSH (International Classification of Diseases codes X60-X84). We analyzed whether there were significant differences among groups that were classified by according to psychiatric diagnosis status, and diagnosis department status, respectively. Another outcome was completed suicide; Cox regression models yielded hazard ratios (HRs) for suicide risk. Patterns were plotted using Kaplan-Meier survival curves. RESULTS There were significant differences in all factors including demographic, health-related, socioeconomic, and survival variables between the groups that were classified by according to psychiatric diagnosis status (p < 0·001). The non-psychiatric diagnosis group had a statistically significant lowest suicide survival rate (81·5%). In comparing the five groups based on whether they received psychiatric diagnosis from psychiatry as well as other departments, there were significant differences in all features (p < 0·001). The group treated at the psychiatric clinic only had highest suicide survival rate (93·4%) significantly. These significant results were confirmed in the Kaplan-Meier survival curves (p < 0·001). The high severity of self-harm tool (HR=4·31; 95% CI, 3·55-5·26) was the most significant effects on suicide risk. CONCLUSIONS Receiving psychiatric evaluation by a medical professional, especially a psychiatrist, is an important protective factor that reduces the suicide rate in the DSH population.