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: 3122
Function: getPubMedXML
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
Accurate detection of cartilage lesions of the knee is required to offer patient-specific care and can alter surgical intervention options. To date, diagnostic arthroscopy remains the gold standard yet often requires the need for staged operative procedure for treatment. Magnetic resonance imaging (MRI) is the most accurate imaging modality with high specificity, yet even with recent advances, MRI has limited specificity. Newer scanners (3T) and updated scanning sequences (3-dimensional MRI and quantitative MRI) are most sensitive in characterizing cartilage lesions of the knee, but these resources are not available to all users. Promising new avenues for patient-specific MRI scans along with the utilization of artificial intelligence will more accurately identify and quantify lesion size, location, and depth.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1016/j.arthro.2024.03.009 | DOI Listing |
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