• Tuberculosis and Respiratory Diseases
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• v.56 no.3
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• pp.280-288
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• 2004

Background : Lung protective strategies, using low tidal volume in ARDS, improve survival rate in ARDS. However, low tidal volume ventilation may promote alveolar de-recruitment. Therefore, alveolar recruitment is necessary to maintain arterial oxygenation and to prevent repetitive opening and closure of collapsed alveoli in lung protective strategies. There has been a recent report describing improvement in arterial oxygenation with use of recruitment maneuver. However, impact of recruitment on outcome of ARDS is unknown. We evaluated whether short-term survival difference existed in patients with ARDS, who were performed alveolar recruitment maneuver(ARM) and prone position, according to response of alveolar recruitment or not. Methods : All patients who were diagnosed with ADRS and received mechanical ventilation were included. ARM were sustained inflation($35-45cmH_2O$ CPAP for 30-40 sec.) or increasing level of PEEP. If these methods were ineffective, alveolar recruitment with prone position was done for at least 10 hours. $P_aO_2/FiO_2$(P/F) ratio was determined before and at 0.5 and 2 hours after ARM. We defined a responder if the P/F ratio was increased over 50% of baseline value. We compared 10-days and 30-days survival rate between responders and non-responders. Results : 20 patients(M:F=12:8, $63{\pm}14age$) were included. Among them, 12 patients were responders and 8 patients were non-responders. In responders, P/F ratio was increased from $92{\pm}25mmHg$ to $244{\pm}85mmHg$. In non-responders, P/F ratio increased from $138{\pm}37mmHg$ to $163{\pm}60mmHg$. Among non-responders, P/F ratio was improved over 50% in 2 patients after prone position. Overall, 14 patients were responders after ARM and prone position. The 10-days and 30-days survival rate in responders was significantly higher than in non-responders(86%, 57% in responders and 33%, 0% in non-responders)(p<0.05). There was no significant difference between responders and non-responders in age($71{\pm}11$, $60{\pm}14$), lung injury score($2.8{\pm}0.2$, $2.9{\pm}0.45$), simplified acute physiology score(SAPS) II ($35{\pm}4.6$, $34{\pm}5.7$), positive end-positive pressure level($15.6{\pm}1.9cmH_2O$, $14.5{\pm}2.1cmH_2O$). Conclusion : ARM may improve arterial oxygenation in some patients with ARDS. These responders in patients with ARDS showed significant higher 10-days and 30-days survival rate than non-responders patients with alveolar recruitment.

• KDI Journal of Economic Policy
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• v.12 no.2
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• pp.3-49
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• 1990

This paper analyzes U.S. demand for imports from Asian NIEs and Japan, utilizing the Almost Ideal Demand System (AIDS) developed by Deaton and Muellbauer, with an emphasis on the effect of changes in the exchange rate. The empirical model assumes a two-stage budgeting process in which the first stage represents the allocation of total U.S. demand among three groups: the Asian NIEs and Japan, six Western developed countries, and the U.S. domestic non-tradables and import competing sector. The second stage represents the allocation of total U.S. imports from the Asian NIEs and Japan among them, by country. According to the AIDS model, the share equation for the Asia NIEs and Japan in U.S. nominal GNP is estimated as a single equation for the first stage. The share equations for those five countries in total U.S. imports are estimated as a system with the general demand restrictions of homogeneity, symmetry and adding-up, together with polynomially distributed lag restrictions. The negativity condition is also satisfied for all cases. The overall results of these complicated estimations, using quarterly data from the first quarter of 1972 to the fourth quarter of 1989, are quite promising in terms of the significance of individual estimators and other statistics. The conclusions drawn from the estimation results and the derived demand elasticities can be summarized as follows: First, the exports of each Asian NIE to the U.S. are competitive with (substitutes for) Japan's exports, while complementary to the exports of fellow NIEs, with the exception of the competitive relation between Hong Kong and Singapore. Second, the exports of each Asian NIE and of Japan to the U.S. are competitive with those of Western developed countries' to the U.S, while they are complementary to the U.S.' non-tradables and import-competing sector. Third, as far as both the first and second stages of budgeting are coneidered, the imports from each Asian NIE and Japan are luxuries in total U.S. consumption. However, when only the second budgeting stage is considered, the imports from Japan and Singapore are luxuries in U.S. imports from the NIEs and Japan, while those of Korea, Taiwan and Hong Kong are necessities. Fourth, the above results may be evidenced more concretely in their implied exchange rate effects. It appears that, in general, a change in the yen-dollar exchange rate will have at least as great an impact, on an NIE's share and volume of exports to the U.S. though in the opposite direction, as a change in the exchange rate of the NIE's own currency $vis-{\grave{a}}-vis$ the dollar. Asian NIEs, therefore, should counteract yen-dollar movements in order to stabilize their exports to the U.S.. More specifically, Korea should depreciate the value of the won relative to the dollar by approximately the same proportion as the depreciation rate of the yen $vis-{\grave{a}}-vis$ the dollar, in order to maintain the volume of Korean exports to the U.S.. In the worst case scenario, Korea should devalue the won by three times the maguitude of the yen's depreciation rate, in order to keep market share in the aforementioned five countries' total exports to the U.S.. Finally, this study provides additional information which may support empirical findings on the competitive relations among the Asian NIEs and Japan. The correlation matrices among the strutures of those five countries' exports to the U.S.. during the 1970s and 1980s were estimated, with the export structure constructed as the shares of each of the 29 industrial sectors' exports as defined by the 3 digit KSIC in total exports to the U.S. from each individual country. In general, the correlation between each of the four Asian NIEs and Japan, and that between Hong Kong and Singapore, are all far below .5, while the ones among the Asian NIEs themselves (except for the one between Hong Kong and Singapore) all greatly exceed .5. If there exists a tendency on the part of the U.S. to import goods in each specific sector from different countries in a relatively constant proportion, the export structures of those countries will probably exhibit a high correlation. To take this hypothesis to the extreme, if the U.S. maintained an absolutely fixed ratio between its imports from any two countries for each of the 29 sectors, the correlation between the export structures of these two countries would be perfect. Therefore, since any two goods purchased in a fixed proportion could be classified as close complements, a high correlation between export structures will imply a complementary relationship between them. Conversely, low correlation would imply a competitive relationship. According to this interpretation, the pattern formed by the correlation coefficients among the five countries' export structures to the U.S. are consistent with the empirical findings of the regression analysis.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.35-52
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• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.

• Journal of Chest Surgery
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• v.38 no.2 s.247
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• pp.116-122
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• 2005

Background: Adverse effects of cardiopulmonary bypass can be avoided by 'Off-pump' coronary artery bypass (OPCAB) surgery. Recent studies have reported that OPCAB had the most beneficial impact on patients at highest risk by reducing bypass-related complications. The purpose of this study is to compare the outcome of OPCAB and conventional coronary artery bypass grafting (CCAB) in patients with poor left ventricular (LV) function. Material and Method: From March 1997 to February 2004, seventy five patients with left ventricular ejection fraction (LVEF) of $35\%$ or less underwent isolated coronary artery bypass grafting at our institute. Of these patients, 33 patients underwent OPCAB and 42 underwent CCAB. Preoperative risk factors, operative and postoperative outcomes, including LV functional change, were compared and analysed. Result: Patients undergoing CCAB were more likely to have unstable angina, three vessel disease and acute myocardial infarction among the preoperative factors. OPCAB group had significantly lower mean operation time, less numbers of total distal anastomoses per patient and less numbers of distal anastomoses per patient in the circumflex territory than the CCAB group. There was no difference between the groups in regard to in-hospital mortality $(OPCAB\; 9.1\%\;(n=3)\;Vs.\;CCAB\;9.5\%\;(n=4)),$ intubation time, the length of stay in intensive care unit and in hospital postoperatively. Postoperative complication occurred more in CCAB group but did not show statistical difference. On follow-up echocardiography, OPCAB group showed $9.1\%$ improvement in mean LVEF, 4.3 mm decrease in mean left ventricular end-diastolic dimension (LVEDD) and 4.2 mm decrease in mean left ventricular end-systolic dimension (LVESD). CCAB group showed $11.0\%$ improvement in mean LVEF, 5.1 mm decrease in mean LVEDD and 5.5 mm decrease in mean LVESD. But there was no statistically significant difference between the two groups. Conclusion: This study showed that LV function improves postoperatively in patients with severe ischemic LV dysfunction, but failed to show any difference in the degree of improvement between OPCAB and CCAB. In terms of operative mortality rate and LV functional recovery, the results of OPCAB were as good as those of CCAB in patients with poor LV function. But, OPCAB procedure was advantageous in shortening of operative time and in decrease of complications. We recommend OPCAB as the first surgical option for patients with severe LV dysfunction.

• Radiation Oncology Journal
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• v.23 no.2
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• pp.61-70
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• 2005

Purpose: Uterine cervix cancer is one of the most prevalent women cancer in Korea. We analysed published papers in Korea with comparing Patterns of Care Study (PCS) articles of United States and Japan for the purpose of developing and processing Korean PCS. Materials and Methods: We searched PCS related foreign-produced papers in the PCS homepage (212 articles and abstracts) and from the Pub Med to find Structure and Process of the PCS. To compare their study with Korean papers, we used the internet site 'Korean Pub Med' to search 99 articles regarding uterine cervix cancer and radiation therapy. We analysed Korean paper by comparing them with selected PCS papers regarding Structure, Process and Outcome and compared their items between the period of before 1980's and 1990's. Results: Evaluable papers were 28 from United States, 10 from the Japan and 73 from the Korea which treated cervix PCS items. PCS papers for United States and Japan commonly stratified into $3\~4$ categories on the bases of the scales characteristics of the facilities, numbers of the patients, doctors, Researchers restricted eligible patients strictly. For the process of the study, they analysed factors regarding pretreatment staging in chronological order, treatment related factors, factors in addition to FIGO staging and treatment machine. Papers in United States dealt with racial characteristics, socioeconomic characteristics of the patients, tumor size (6), and bilaterality of parametrial or pelvic side wail invasion (5), whereas papers from Japan treated of the tumor markers. The common trend in the process of staging work-up was decreased use of lymphangiogram, barium enema and increased use of CT and MRI over the times. The recent subject from the Korean papers dealt with concurrent chemoradiotherapy (9 papers), treatment duration (4), tumor markers (B) and unconventional fractionation. Conclusion: By comparing papers among 3 nations, we collected items for Korean uterine cervix cancer PCS. By consensus meeting and close communication, survey items for cervix cancer PCS were developed to measure structure, process and outcome of the radiation treatment of the cervix cancer. Subsequent future research will focus on the use of brachytherapy and its impact on outcome including complications. These finding and future PCS studies will direct the development of educational programs aimed at correcting identified deficits in care.