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
Optimal pain management is essential for good care outcomes, but assessing pain is particularly complex in intensive care, as patients are often unable to communicate. We hypothesize that the task could be supported through human language technology. To evaluate the feasibility of such tools, we study how pain is documented in electronic Finnish free-text intensive care nursing notes by statistically comparing annotations of ten nursing professionals on a set of 1548 documents. The aspects considered include the amount and writing style of pain-related notes, pain intensity, and given pain care. More than half of the documents contained information relevant for patients' pain status but it was expressed usually indirectly. Also pain medication was commented as free-text. Although annotators' pain intensity evaluations diverged, the substantial amount of pain-related notes encourages developing computational tools for pain assessment.
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