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| 1 | Effect of liver cirrhosis on long-term outcomes after acute respiratory failure: A population-based study显示文摘AIM To assessed the effect of liver cirrhosis(LC) on the poorly understood long-term mortality risk after firstever mechanical ventilation(1-MV) for acute respiratory failure.METHODS All patients in Taiwan given a 1-MV between 1997 and 2013 were identified in Taiwan's Longitudinal Health Insurance Database 2000. Each patient with LC was individually matched, using a propensity-score method, to two patients without LC. The primary outcome was death after a 1-MV.RESULTS A total of 16653 patients were enrolled: 5551 LC-positive (LC^([Pos])) patients, including 1732 with cryptogenic LCs and 11102 LC-negative(LC^([Pos])) controls. LC^([Pos]) patients had more organ failures and were more likely to be admitted to medical department than were LC^([Pos]) controls. LC^([Pos]) patients had a significantly lower survival rate(AHR = 1.38, 95%CI: 1.32-1.44). Moreover, the mortality risk was significantly higher for patients with non-cryptogenic LC than for patients with cryptogenic LC(AHR = 1.43, 95%CI: 1.32-1.54) and patients without LC(AHR = 1.56, 95%CI: 1.32-1.54). However, there was no significant difference between patients with cryptogenic and without LC(HR = 1.05, 95%CI: 0.98-1.12).CONCLUSION LC, especially non-cryptogenic LC, significantly increases the risk of death after a 1-MV. | Chih-Cheng Lai Chung-Han Ho Kuo-Chen Cheng Chien-Ming Chao Chin-Ming Chen Willy Chou | 2017 | World Journal of Gastroenterology2017，23，12： | 4 |
| 2 | Role of physiother- apy and patient education in lymphedema control following breast cancersurgery显示文摘 | Shiang Ru lu Rong Binhong Willy Chou | 2015 | Therapeutics and Clinical Risk Man- agement2015，2，： | 1 |
| 3 | Detecting Dengue Fever in Children: Using Sequencing Symptom Patterns for An Online Assessment Approach显示文摘Background: Dengue fever (DF) is an important health problem in Asia. We examined it using its clinical symptoms to predict DF. Methods: We extracted statistically significant features from 17 DF-related clinical symptoms in 177 pediatric patients (69 diagnosed with DF) using the unweighted summation score and the non-parametric HT person fit statistic, which jointly combine the weighted score (yielded by logistic regression) to predict DF risk. Results: Six symptoms (Family History, Fever 39C, Skin Rash, Petechiae, Abdominal Pain, and Weakness) significantly predicted DF. When a cutoff point of 1.03 (p = 0.26) suggested combining the weighted score and the HT coefficient, the sensitivity was 0.91 and the specificity was 0.76. The area under the ROC curve was 0.88, which was a better predictor: specificity was 5.56% higher than for the traditional logistic regression. Conclusions: Six simple symptoms analyzed using logistic regression were useful and valid for early detection of DF risk in children. A better predictive specificity increased after combining the non-parametric HT coefficient to the weighted regression score. A self-assessment using patient smart phones is available to discriminate DF and may eliminate the need for a costly and time-consuming dengue laboratory test. | Tsair-Wei Chien Julie Chi Chow Yu Chang Willy Chou | 2018 | Advances in Health and Behavior2018，1，1： | 0 |
| 4 | The most highly-cited authors who published papers on the topic of health behavior: A Bibliometric Analysis显示文摘Background: Health behavior (HB) is an action taken by a person who pursues good health and prevents illness. Health behavior, thus, reflects a person’s health beliefs and attracts, particularly, on published papers in academics. However, who is the most influential author (MIA) with highly-cited papers on HB remains unknown. Objective: The purpose of this study is to apply the authorship-weighted scheme (AWS) developed by authors to select the MIA on HB using the visual displays on Google Maps. Methods: We obtained 1,116 abstracts published between 2012 and 2016 from Medline based on the keywords of (health [Title]) and (behavior [Title] or behavior [Title]) on September 22, 2018. The author names, countries/areas, and Pubmed paper IDs were recorded. The AWS was applied to (1) select the most productive authors (MPA) using social network analysis (SNA);(2) discover the MIA using h-indexes and author impact factors (AIF) dispersed on Google Maps, and (3) display the countries/areas distributed for the x-index in geography. Pajek software was performed to determine the partition categories of clusters. Results: We found that the MPA and MIA are Matthew K Nock (US) and Erika A Waters (US) for the MPA and MIA, respectively. All visual representations that are the form of a dashboard can be easily displayed on Google Maps. The most influential countries are the US (=19.03) and Australia (=6.46) with the highest x-indexes. Readers are suggested to manipulate them on their own on Google Maps. Conclusion: Many individual researchers achievements (IRA) were determined using h-index, AIF, x-index, or other bibliometric indices without quantifying author contributions. We demonstrated visualized representations on Google Maps using the AWS developed by authors to measure authors influences in a specific discipline. The research approach using the AWS to quantify the authors contributions can be applied to measure IRA in the future. | Chen Fang Hsu Tsair Wei Chien Julie Chi Chow Willy Chou | 2018 | Advances in Health and Behavior2018，1，1： | 0 |
| 5 | The most cited articles on the topic of health behaviors in Google Trends research: a systematic review显示文摘Background: Over the past decade, the use of Web-based data in public health issues has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information and has been applied to several topics with the most focused subject related to health and medicine. However, the most cited articles and the popular medical subject headings (MESH terms) on health behaviors in Google Trends research remain unknown. The web-based behavior requires to monitor and analyze on-line data for examining actual human behavior to predict and even prevent health-related issues that constantly arise in daily life. Objective: This systematic review aimed at reporting and further presenting the most cited articles and the popular MESH terms on health behaviors in Google Trends (infodemiology) researches in health-related topics since 2009 to provide an overview of the topic burst for future research on the subject of health behavior. Methods: Following the Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends [Title]” in PubMed databases since 2009, applying specific criteria for types of journal articles. A total of 86 published papers were extracted, excluding those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to MESH terms using social network analysis (SNA) and selected the most cited articles that related to the health behavior in Google Trends. Results: The most cited articles are those from the US in 2009 (PMID= 19845471 cited 88 times) and the UK in 2013 (PMID= 23619126 cited 74 times). The MESH term represented by Internet earns the highest impact factor (IF) and presents significantly different among term clusters (F (3,20)=15.79, p<0.001). The most number of citing journals is from PloS One. The most number of author affiliations is from the US. Conclusion: The monitoring of online queries can provide insight into human behavior, as the phenomenon is significantly and continuously growing at present and in the future for assessing behavioral changes in health topics. | Wei Chih Kan Tsair Wei Chien Hsien Yi Wang Willy Chou | 2018 | Advances in Generial Practice of Medicine2018，1，1： | 0 |
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