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
Background: Searching for topics within large biomedical databases can be challenging, especially when topics are complex, diffuse, emerging or lack definitional clarity. Experimentally derived topic search filters offer a reliable solution to effective retrieval; however, their number and range of subject foci remain unknown.
Objectives: This systematic scoping review aims to identify and describe available experimentally developed topic search filters.
Methods: Reports on topic search filter development (1990-) were sought using grey literature sources and 15 databases. Reports describing the conception and prospective development of a database-specific topic search and including an objectively measured estimate of its performance ('sensitivity') were included.
Results: Fifty-four reports met inclusion criteria. Data were extracted and thematically synthesised to describe the characteristics of 58 topic search filters.
Discussion: Topic search filters are proliferating and cover a wide range of subjects. Filter reports, however, often lack clear definitions of concepts and topic scope to guide users. Without standardised terminology, filters are challenging to find. Information specialists may benefit from a centralised topic filter repository and appraisal checklists to facilitate quality assessment.
Conclusion: Findings will help information specialists identify existing topic search filters and assist filter developers to build on current knowledge in the field.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1111/hir.12244 | DOI Listing |
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