echemi logo
Product
  • Product
  • Supplier
  • Inquiry
    Home > Active Ingredient News > Study of Nervous System > Stroke: How to quickly classify upper limb symptoms for stroke?

    Stroke: How to quickly classify upper limb symptoms for stroke?

    • Last Update: 2021-10-10
    • Source: Internet
    • Author: User
    Search more information of high quality chemicals, good prices and reliable suppliers, visit www.echemi.com

    Upper limb motor function recovery for brain stroke patients regain independence is crucial
    .


    ARAT (Action Research Arm Test) is a personal assessment of the ability of the upper limbs and is often used in stroke rehabilitation research


    Stroke

    In a sample of 131 patients, Nijland et al.
    defined the cutoff scores for ARAT's 3 types of upper extremity outcomes at 6 months after stroke
    .


    These authors later defined cutoff scores for the five categories of upper limb outcomes 6 months after stroke in a sample of 460 patients, including 131 patients in the original three-category classification system


    A hypothesis-free cluster analysis was used to determine the scores of 4 types of ARAT on the scores obtained by 40 patients 3 months after stroke



    In this way, Harry T.


    Jordan and others of the University of Auckland in Information Theory used a subset of the ARAT task to derive and internally verify the decision tree to detect subacute (3-6 months after stroke) and chronic (stroke) After >6 months), the outcome of upper limb movement is classified


    The video rapid and potential remote FOCUS assessment can be used to evaluate the results in clinical practice and screen potential participants for the trial


    They found that the decision tree generated by classification and regression tree analyses requires 2 to 4 ARAT tasks
    .


    The overall accuracy of the cross-validated decision tree is between 87.
    7% (SE, 1.
    0%) and 96.
    7% (SE, 2.
    0%)


    When the patient is classified into one of the three outcome categories, the accuracy is the highest, and the accuracy of the five categories is the lowest
    .


    Decision trees are called FOCUS (Fast Outcome Classification of Upper Limbs after Stroke) assessments, and they are still accurate for ARAT scores 6 months after stroke (overall accuracy range is 83.
    4%-91.
    7%)


    The important significance of this research lies in the discovery: A subset of the ARAT task can accurately classify the results of upper limb movement after stroke
    .


    Future research can study the feasibility and accuracy of using FOCUS to evaluate the classification of results remotely through video calls


    A subset of the ARAT task can accurately classify the results of upper limb movement after stroke

    Original Source:
    Jordan HT, Che J, Byblow WD, Stinear CM.


    Fast Outcome Categorization of the Upper Limb After Stroke.
    Stroke.
    Published online October 4, 2021:STROKEAHA.
    121.
    035170.
    doi:10.
    1161/STROKEAHA.
    121.
    035170


    Fast Outcome Categorization of the Upper Limb After Stroke.
     

    Leave a message here
    This article is an English version of an article which is originally in the Chinese language on echemi.com and is provided for information purposes only. This website makes no representation or warranty of any kind, either expressed or implied, as to the accuracy, completeness ownership or reliability of the article or any translations thereof. If you have any concerns or complaints relating to the article, please send an email, providing a detailed description of the concern or complaint, to service@echemi.com. A staff member will contact you within 5 working days. Once verified, infringing content will be removed immediately.

    Contact Us

    The source of this page with content of products and services is from Internet, which doesn't represent ECHEMI's opinion. If you have any queries, please write to service@echemi.com. It will be replied within 5 days.

    Moreover, if you find any instances of plagiarism from the page, please send email to service@echemi.com with relevant evidence.