Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Alzheimer's disease (AD) affects neurological, cognitive, and behavioral processes. Thus, to accurately assess this disease, researchers and clinicians need to combine and incorporate data across these domains. This presents not only distinct methodological and statistical challenges but also unique opportunities for the development and advancement of psychometric techniques. In this article, we describe relatively recent research using item response theory (IRT) that has been used to make progress in assessing the disease across its various symptomatic and pathological manifestations. We focus on applications of IRT to improve scoring, test development (including cross-validation and adaptation), and linking and calibration. We conclude by describing potential future multidimensional applications of IRT techniques that may improve the precision with which AD is measured.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1177/1073191117745125 | DOI Listing |
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