• Korean Journal of Clinical Pharmacy
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• v.25 no.1
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• pp.9-17
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• 2015

Background: Biologic disease-modifying antirheumatic drugs (bDMARDs) extend the treatment choices for rheumatoid arthritis patients with insufficient response or intolerance to conventional DMARDs (cDMARDs). These agents have considerable efficacy compared with conventional DMARDs, but only a few head-to-head comparisons among these agents have been performed. The objective of this systematic review and network meta-analysis (NMA) was to compare the relative efficacy of Certolizumab with conventional DMARD to licensed bDMARD with cDMARD therapy for patients who failed to prior cDMARD treatment under the condition of the reimbursement coverage criteria in Korea. Methods: A systematic review was conducted using MEDLINE and Cochrane library. Key endpoints were the American College of Rheumatology (ACR) responses of 20/50/70 at six months. Bayesian outcomes were calculated as median of treatment effect, probability of the best, Odds Ratio (OR) and probability that OR was greater than one. Results: Compared with other bDMARDs, Certolizumab were associated with higher or comparable ACR response rates; in ACR20, the OR (probability of OR>1) was 2.08 (92.6%) for Adalimumab, 1.86 (85.7%) for Etanercept, 1.89 (79.5%) for Golimumab, 2.36 (92.1%) for Infliximab, 1.79 (87.0%) for Abatacept, 1.74 (80.8%) for Rituximab and 1.82 (86.8%) for Tocilizaumab. In ACR50 and ACR70, the ORs did not present significant differences. Conclusion: Certolizaumab with cDMARD was more effective or comparable than other bDMARDs in patients who failed prior cDMARD treatment.

• Informatization Policy
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• v.19 no.4
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• pp.3-26
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• 2012

The purpose of this study is to provide a systematical view point of SNS research published by scholarly journals in communications vis a meta-analysis. A total of 72 articles dealing with SNS issues from 2006 to 2012 were analysed by research theme, related sub-theme, methodology, theoretic rationale, targeted media, and characteristics. Effect research get the largest portion and then user research in terms of a developmental model of media research agenda. A major methodological trend is an online survey and theoretical background is Uses and Gratification. Twitter is the most popular medium researched and SNS is mainly characterized as information provision and seeking as well as relationship forming.

• The Korean Journal of Pain
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• v.34 no.4
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• pp.509-533
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• 2021

Background: Postherpetic neuralgia (PHN) is a refractory complication of herpes zoster (HZ). To prevent PHN, various strategies have been aggressively adopted. However, the efficacy of these strategies remains controversial. Therefore, we aimed to estimate the relative efficacy of various strategies used in clinical practice for preventing PHN using a network meta-analysis (NMA). Methods: We performed a systematic and comprehensive search to identify all randomized controlled trials. The primary outcome was the incidence of PHN at 3 months after acute HZ. We performed both frequentist and Bayesian NMA and used the surface under the cumulative ranking curve (SUCRA) values to rank the interventions evaluated. Results: In total, 39 studies were included in the systematic review and NMA. According to the SUCRA value, the incidence of PHN was lower in the order of continuous epidural block with local anesthetics and steroids (EPI-LSE), antiviral agents with subcutaneous injection of local anesthetics and steroids (AV + sLS), antiviral agents with intracutaenous injection of local anesthetics and steroids (AV + iLS) at 3 months after acute HZ. EPI-LSE, AV + sLS and AV + iLS were also effective in preventing PHN at 1 month after acute HZ. And paravertebral block combined with antiviral and antiepileptic agents was effective in preventing PHN at 1, 3, and 6 months. Conclusions: The continuous epidural block with local anesthetics and steroid, antiviral agents with intracutaneous or subcutaneous injection of local anesthetics and a steroid, and paravertebral block combined with antiviral and antiepileptic agents are effective in preventing PHN.

• Korea Trade Review
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• v.45 no.6
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• pp.207-225
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• 2020

Korea has studied FTA over 20 years since FTA with Chile. More than 3,000 Articles have been published in journal so far. Thus, this study aims to present the direction that should be taken by Korean FTA study by using Meta-Analysis for FTA study which has been carried on so far. Many researchers publish several articles each year, showing high quotation index and H-index. However, Korean FTA study lacks network with overseas researchers and fusion study with other sciences. 65.4% of Korean FTA study is carried on Independent research and 91.1% of them, in joint research by fewer than 2 persons. Further, the Subject of FTA study is not diverse and too uniform. Out of total studies, 24.3% of them are related to rules of origin and 15.3% of them, to China, showing that theme of study is quite partially concentrated. This is large difference with overseas FTA study. Study on rules of origin is only 1.5% in overseas. Korean FTA study needs to diversify subjects of study and to balance between academic aspect & practical aspect. When it comes to study methodology empirical analysis assumed large portion in both Korea and overseas countries. Empirical analysis assumes 18.3% in Korea and 47.3% in overseas, both of which are quite high. However, qualitative study such as FGI/AHP, in-depth interview, case analysis is quite rare in Korean FTA study. Partial concentration of countries for study subject needs to be rectified also. In Korea, countries for FTA study is China 15.3%, EU 10.0%, USA 6.3%. In overseas, China assumes only 3.7% of study subject. It is required for Korean FTA study to extend study subjects & study area by forming global study network and to extend qualitative study with microscopic study.

• Journal of Korean Library and Information Science Society
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• v.33 no.2
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• pp.109-137
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• 2002

A literature analysis for the planning and realization of meta database system was carried out to establish the ocean electronic resources, the first in Korea. The study targeted from web resources and to oceanographic survey data. The focus of the analysis lies in the providing practical information retrieval service for ocean electronic resources based on the framework of effective Dublin Core metadata with network resources description. The analyses included ocean electronic resources, metadata descriptive elements, metadata classification, system organization and retrieval for planning and implementation of meta database system.

• Journal of the Korean Society for information Management
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• v.21 no.4 s.54
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• pp.5-26
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• 2004

A literature analysis for the planning and realization of the multimedia meta database and digital library's integrated information system was carried out to establish the various oceanographic resources in the Oceanographic Information Center, the first in Korea. The study targeted from printed matter, network resources, full-text and to VOD. The focus of the analysis lies in the providing practical integrated information retrieval service for oceanographic resources based on the framework of effective MODS metadata with network resources description. The analyses included oceanographic resources, multimedia information processing, MODS metadata descriptive elements, metadata classification, system organization, and retrieval for planning and implementation of the multimedia meta database system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.6
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• Korean Journal of Culture and Social Issue
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• v.30 no.3
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• pp.239-265
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• 2024

The purpose of this study was to compare the predictive accuracy of traditional prediction models (methods) and machine learning algorithms in predicting suicidal behaviors. The research aimed to go beyond a systematic review level and scientifically examine the predictive capabilities of these two techniques through meta-analysis, analyzing variables identified through domestic research, particularly at the regional level. In order to achieve this, a total of 124 studies, including 50 studies utilizing machine learning and 74 studies employing traditional methods, were included in the meta-analysis. The results of the study revealed that the integrated area under the curve (AUC) for studies using traditional methods was .770, which was lower than the integrated AUC value of .853 for studies using machine learning. Particularly, studies conducted in Asia (AUC = .944) demonstrated higher accuracy compared to studies in Western countries (AUC = .820) and Korea (AUC = .864). Additional analysis of the moderating effects in domestic research indicated that a higher proportion of males and the prediction of suicide attempts were associated with higher prediction accuracy. On the other hand, prediction accuracy was lower when the prediction target was suicide deaths and when studies utilized neural network analysis. This study synthesized various research findings on the prediction of suicidal behaviors, verified the effectiveness of prediction using machine learning, and holds significance in exploring variables applicable in the context of South Korea.

• Knowledge Management Research
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• v.23 no.2
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• pp.37-60
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• 2022

The growth of virtual reality games and the popularization of blockchain technology are bringing significant changes to the formation of the metaverse industry ecosystem. Especially, after Meta acquired Oculus, a VR device and application company, the growth of VR-based metaverse services is accelerating. In this study, the concept that supports leisure activities in the metaverse environment is explored realting to game-like features in VR apps, which differentiates traditional mobile apps based on a smart phone device. Using exploratory text mining methods and network analysis approches, 241 apps registed in the Oculus Quest 2 App Store were analyzed. Analysis results from a quasi-network show that a leisure concept is closely related to various genre features including a game and tourism. Additionally, the anlaysis results of G & F model indicate that the leisure concept is distictive in the view of gateway brokerage role. Those results were also confirmed in LDA topic modeling analysis.

• Imaging Science in Dentistry
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• v.54 no.3
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• pp.232-239
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• 2024

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.