WeChat Mini Program
Old Version Features

    • Prognostic Value of Triglyceride/glucose Index in Patients with ST-segment Elevation Myocardial Infarction.

    Flora Ozkalayci,Ali Karagoz,Can Y. Karabay,Ibrahim H. Tanboga,Erdem Turkyilaz,Mehmet Saygi,Vecih Oduncu

    BIOMARKERS IN MEDICINE（2022）

    Hisar Intercontinental Hosp

    Cited 8|Views15
    Abstract
    Aim: New parameters are emerging to predict prognosis in patients with ST-segment elevation myocardial infarction (STEMI). In this study we aimed to determine and compare the prognostic values of some metabolic indices in terms of predicting long-term mortality in patients with STEMI. Method: A total of 1900 nondiabetic patients who presented with STEMI and underwent percutaneous coronary intervention were included in the study. Multivariable Cox proportional regression analysis was used to determine and compare the predictive performance of triglyceride-glucose (TyG) index, triglyceride-high-density lipoprotein ratio (Ty/HDL) and admission glucose. Results: In multivariable Cox regression analysis, the model based on TyG index had better predictive performance than the Ty/HDL and admission blood glucose. Conclusion: The TyG index is more informative than Ty/HDL and admission glucose level to predict long-term all-cause mortality.
    More
    Translated text
    Key words
    metabolic index,ST-elevation MI,triglyceride-glucose index,triglyceride-HDL index
    上传PDF
    View via Publisher
    Bibtex
    收藏
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined