A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 143

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Predictive factors for walking in acute stroke patients: a multicenter study using classification and regression tree analysis. | LitMetric

AI Article Synopsis

  • The study aimed to create a prediction model for independent walking in acute stroke patients using bedside assessments and classification techniques.
  • Conducted on 240 stroke patients, the research focused on factors like age, gender, stroke severity, and recovery abilities to classify patients into independent and dependent walking groups.
  • The results identified key indicators for walking ability, categorizing patients based on their motor function and cognitive status, ultimately helping to predict their potential for independent walking after a stroke.*

Article Abstract

[Purpose] Walking ability should be predicted as early as possible in acute stroke patients. The purpose is to construct a prediction model for independent walking from bedside assessments using classification and regression tree analysis. [Participants and Methods] We conducted a multicenter case-control study with 240 stroke patients. Survey items included age, gender, injured hemisphere, the National Institute of Health Stroke Scale, the Brunnstrom Recovery Stage for lower extremities, and "turn over from a supine position" from the Ability for Basic Movement Scale. The National Institute of Health Stroke Scale items, such as language, extinction, and inattention, were grouped under higher brain dysfunction. We used the Functional Ambulation Categories to classify patients into independent (four or more the Functional Ambulation Categories; n=120) and dependent (three or fewer the Functional Ambulation Categories; n=120) walking groups. A classification and regression tree analysis was used to create a model to predict independent walking. [Results] The Brunnstrom Recovery Stage for lower extremities, "turn over from a supine position" from the Ability for Basic Movement Scale, and higher brain dysfunction were the splitting criteria for classifying patients into four categories: Category 1 (0%), severe motor paresis; Category 2 (10.0%), mild motor paresis and could not turn over; Category 3 (52.5%), with mild motor paresis, could turn over, and had higher brain dysfunction; and Category 4 (82.5%), with mild motor paresis, could turn over, and no higher brain dysfunction. [Conclusion] We constructed a useful prediction model for independent walking based on the three criteria.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974313PMC
http://dx.doi.org/10.1589/jpts.35.217DOI Listing

Publication Analysis

Top Keywords

higher brain
16
brain dysfunction
16
motor paresis
16
stroke patients
12
classification regression
12
regression tree
12
tree analysis
12
independent walking
12
functional ambulation
12
ambulation categories
12

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!