• Journal of the Korean Orthopaedic Association
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• v.54 no.5
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• pp.427-434
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• 2019

Purpose: This study evaluated the long term clinical and radiographic results and the survival rates of unicompartmental knee arthroplasty (UKA). In addition, the factors affecting the survival of the procedure were analyzed and the survival curve was compared according to the affecting factors. Materials and Methods: Ninety-nine cases of UKA performed between December 1982 and January 1996 were involved: 10 cases with Modular II, 44 cases with Microloc, and 45 cases with Allegretto prostheses. The mean follow-up period was 16.5 years. Clinically, the hospital for special surgery (HSS) scoring system and the range of motion (ROM) were evaluated. Radiographically, the femorotibial angle (FTA) was measured. The survival rate was analyzed using the Kaplan-Meier method. Cox regression analysis was used to identify the factors affecting the survival according to age, sex, body mass index, preoperative diagnosis, and type of implant. The Kaplan-Meier survival curves were compared according to the factors affecting the survival of UKA. Results: The overall average HSS score and ROM was 57.7 and 134.3° preoperatively, 92.7 and 138.4° at 1 year postoperatively, and 79.1 and 138.4° at the last follow-up (p<0.001, respectively). The overall average FTA was varus 0.8° preoperatively, valgus 4.1° at postoperative 2 weeks, and valgus 3.0° at the last follow-up. The overall 5-, 10-, 15- and 20-year survival rates were 91.8%, 82.9%, 71.0%, and 67.0%, respectively. The factors affecting the survival were the age and type of implant. The risk of the failure decreased with age (hazard ratio=0.933). The Microloc group was more hazardous than the other prostheses (hazard ratio=0.202, 0.430, respectively). The survival curve in the patients below 60 years of age was significantly lower than those of the patients over 60 years of age (p=0.003); the survival curve of the Microloc group was lower compared to the Modular II and Allegretto groups (p=0.025). Conclusion: The long-term clinical and radiographic results and survival of UKA using old fixed bearing prostheses were satisfactory. The selection of appropriate patient and prosthesis will be important for the long term survival of the UKA procedure.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.28 no.1
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• pp.70-79
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• 2024

Backgrounds/Aims: After pancreatoduodenectomy (PD), an early oral diet is recommended; however, the postoperative nutritional management of PD patients is known to be highly variable, with some centers still routinely providing parenteral nutrition (PN). Some patients who receive PN experience clinically significant complications, underscoring its judicious use. Using a large cohort, this study aimed to determine the proportion of PD patients who received postoperative nutritional support (NS), describe the nature of this support, and investigate whether receiving PN correlated with adverse perioperative outcomes. Methods: Data were extracted from the Recurrence After Whipple's study, a retrospective multicenter study of PD outcomes. Results: In total, 1,323 patients (89%) had data on their postoperative NS status available. Of these, 45% received postoperative NS, which was "enteral only," "parenteral only," and "enteral and parenteral" in 44%, 35%, and 21% of cases, respectively. Body mass index < 18.5 kg/m² (p = 0.03), absence of preoperative biliary stenting (p = 0.009), and serum albumin < 36 g/L (p = 0.009) all correlated with receiving postoperative NS. Among those who did not develop a serious postoperative complication, i.e., those who had a relatively uneventful recovery, 20% received PN. Conclusions: A considerable number of patients who had an uneventful recovery received PN. PN is not without risk, and should be reserved for those who are unable to take an oral diet. PD patients should undergo pre- and postoperative assessment by nutrition professionals to ensure they are managed appropriately, and to optimize perioperative outcomes.

• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.151-163
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• 2024

This study sought to confirm the impact of analytical methods and behavioral economic theory factors on decision-making when making chartering decisions in the dry bulk shipping market. This study on chartering decision-making model was began to verify why shipping companies do not make rational decision-making and behavior based on analytical methods such as freight prediction and process of alternative selection in the same market situation. To understand the chartering decision-making model, it is necessary to study the impact of behavioral economic theory such as heuristics, loss aversion, and herding behavior on chartering decision-making. Through AHP analysis, the importance of the method factors relied upon in chartering decision-making. The dependence of the top factors in chartering decision-making was in the following order: market factors, heuristics, internal factors, herding behavior, and loss aversion. Market factors, heuristics, and internal factors. As for detailed factors, spot freight index and empirical intuition were confirmed as the most important factors relied on when making decisions. It was confirmed that empirical intuition is more important than internal analysis, which is an analytical method. This study can be said to be meaningful in that it academically researched and proved the bounded rationality of humans, which cannot be fully rational, and sometimes relies on experience or psychological tendencies, by applying it to the chartering decision-making model in the dry bulk shipping market. It also suggests that in the dry bulk shipping market, which is uncertain and has a high risk of loss due to decision-making, the experience and insight of decision makers have a very important impact on the performance and business profits of the operation part of shipping companies. Even though chartering are a decision-making field that requires judgment and intuition based on heuristics, decision-makers need to be aware of this decision-making model in order to reduce repeated mistakes of deciding contrary to market situation. It also suggests that there is a need to internally research analytical methods and procedures that can complement heuristics such as empirical intuition.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.355-380
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• 2004

I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

• Tuberculosis and Respiratory Diseases
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• v.42 no.6
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• pp.871-877
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• 1995

Background: The index which could predict the prognosis of critically ill patients is needed to find out high risk patients and to individualize their treatment. The APACHE III scoring system was established in 1991, but there has been only a few studies concerning its prognostic value. We wanted to know whether the APACHE III scores have prognostic value in discriminating survivors from nonsurvivors in sepsis. Methods: In 48 patients meeting the Bones criteria for sepsis, we retrospectively surveyed the day 1(D1), day 2(D2) and day 3(D3) scores of patients who were admitted to intensive care unit. The scores of the sepsis survivors and nonsurvivors were compared in respect to the D1 score, and also in respect to the changes of the updated D2 and D3 scores. Results: 1) Of the 48 sepsis patients, 21(43.5%) survived and 27(56.5%) died. The nonsurvivors were older($62.7{\pm}12.6$ vs $51.1{\pm}18.1$ yrs), presented with lower mean arterial pressure($56.9{\pm}26.2$ vs $67.7{\pm}14.2\;mmHg$) and showed greater number of multisystem organ failure($1.2{\pm}0.8$ vs $0.2{\pm}0.4$) than the survivors(p<0.05, respectively). There were no significant differences in sex and initial body temperature between the two groups. 2) The D1 score was lower in the survivors (n=21) than in the nonsurvivors ($44.1{\pm}14.6$, $78.5{\pm}18.6$, p=0.0001). The D2 and D3 scores significantly decreased in the survivors (D1 vs D2, $44.1{\pm}14.6$ : $37.9{\pm}15.0$, p=0.035; D2 vs D3, $37.9{\pm}15.0$ : $30.1{\pm}9.3$, p=0.0001) but showed a tendency to increase in the nonsurvivors (D1 vs D2 (n=21), $78.5{\pm}18.6$ : $81.3{\pm}23.0$, p=0.1337; D2 vs D3 (n=11), $68.2{\pm}19.3$ : $75.3{\pm}18.8$, p=0.0078). 3) The D1 scores of 12 survivors and 6 nonsurvivors were in the same range of 42~67 (mean D1 score, $53.8{\pm}10.0$ in the survivors, $55.3{\pm}10.3$ in the nonsurvivors). The age, sex, initial body temperature, and mean arterial pressure were not different between the two groups. In this group, however, D2 and D3 was significantly decreased in the survivors(D1 vs D2, $53.3{\pm}10.0$ : $43.6{\pm}16.4$, p=0.0278; D2 vs D3, $43.6{\pm}16.4$ : $31.2{\pm}10.3$, p=0.0005), but showed a tendency to increase in the nonsurvivors(D1 vs D2 (n=6), $55.3{\pm}10.3:66.7{\pm}13.9$, p=0.1562; D2 vs D3 (n=4), $64.0{\pm}16.4:74.3{\pm}18.6$, p=0.1250). Among the individual items of the first day APACHE III score, only the score of respiratory rate was capable of discriminating the nonsurvivors from the survivors ($5.5{\pm}2.9$ vs $1.9{\pm}3.7$, p=0.046) in this group. Conclusion: In sepsis, nonsurvivors had higher first day APACHE III score and their updated scores on the following days failed to decline but showed a tendency to increase. Survivors, on the other hand, had lower first day score and showed decline in the updated APACHE scores. These results suggest that the first day and daily updated APACHE III scores are useful in predicting the outcome and assessing the response to management in patients with sepsis.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.