Background: Although total knee arthroplasty (TKA) is considered an effective treatment for knee osteoarthritis, it carries risks of complications. With a growing number of TKAs performed on older patients, understanding the cause of mortality is crucial to enhance the safety of TKA. This study aimed to identify the major causes of short- and long-term mortality after TKA and report mortality trends for major causes of death. Methods: A total of 4,124 patients who underwent TKA were analyzed. The average age at surgery was 70.7 years. The average follow-up time was 73.5 months. The causes of death were retrospectively collected through Korean Statistical Information Service and classified into 13 subgroups based on the International Classification of Diseases-10 code. The short- and long-term causes of death were identified within the time-to-death intervals of 30, 60, 90, 180, 180 days, and > 180 days. Standard mortality ratios (SMRs) and cumulative incidence of deaths were computed to examine mortality trends after TKA. Results: The short-term mortality rate was 0.07% for 30 days, 0.1% for 60 days, 0.2% for 90 days, and 0.2% for 180 days. Malignant neoplasm and cardiovascular disease were the main short-term causes of death. The long-term (> 180 days) mortality rate was 6.2%. Malignant neoplasm (35%), others (11.7%), and respiratory disease (10.1%) were the major long-term causes of death.Men had a higher cumulative risk of death for respiratory, metabolic, and cardiovascular diseases. Age-adjusted mortality was significantly higher in TKA patients aged 70 years (SMR, 4.3; 95% confidence interval [CI], 3.3–5.4) and between 70 and 79 years (SMR 2.9; 95% CI, 2.5–3.5) than that in the general population. Conclusions The short-term mortality rate after TKA was low, and most of the causes were unrelated to TKA. The major causes of long-term death were consistent with previous findings. Our findings can be used as counseling data to understand the survival and mortality of TKA patients.

OBJECTIVES: Although there have been studies that examine sex differences of the brain structures using magnetic resonance imaging, studies that specifically investigate cerebellar structural differences between men and women are scarce. The purpose of current study was to examine sex differences in structures of the cerebellum using cerebellar template and cerebellum analysis methods. METHODS: Sixteen men and twenty women were included in the study. A MATLAB based program (MathWorks, Natick, MA, USA), Statistical Parametric Mapping 5 (SPM5) using the spatially unbiased infra-tentorial atlas template (SUIT) as the cerebellum template, was used to analyze the brain imaging data. RESULTS: There was no significant difference in age between men (mean age = 28.1) and women (mean age = 27.2). Men showed higher gray matter density than women in two left cerebellar areas including the clusters in the lobules IV and V (a cluster located across the lobules IV and V), and the lobule VIIIb (lobules IV and V, t = 4.75, p < 0.001 ; lobule VIIIb, t = 3.08, p = 0.004). CONCLUSIONS: The current study found differences in cerebellar gray matter density between men and women. The current study holds its significance for applying the template specifically developed for the analysis of cerebellum.
Adult ; Brain ; Cerebellum ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Neuroimaging ; Sex Characteristics

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.