Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1186/s13643-018-0724-7 http://scihub22266oqcxt.onion/10.1186/s13643-018-0724-7 C5918752!5918752!29695296     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Syst+Rev 2018 ; 7 (ä): ä Nephropedia Template TP {{tp|p=29695296|t=2018. Automated screening of research studies for systematic reviews using study characteristics |pdf=|usr=}}{{29695296}} gab.com Text Automated screening of research studies for systematic reviews using study characteristics JO: Syst Rev http://www.kidney.de/pget.php?&tw=1&pmid=29695296 #FOAvip Background: Screening candidate studies for inclusion in a systematic review is time-consuming when conducted manually. Automation tools could reduce the human effort devoted to screening. Existing methods use supervised machine learning which train classifiers to identify relevant words in the abstracts of candidate articles that have previously been labelled by a human reviewer for inclusion or exclusion. Such classifiers typically reduce the number of abstracts requiring manual screening by about 50%. Methods: We extracted four key characteristics of observational studies (population, exposure, confounders and outcomes) from the text of titles and abstracts for all articles retrieved using search strategies from systematic reviews. Our screening method excluded studies if they did not meet a predefined set of characteristics. The method was evaluated using three systematic reviews. Screening results were compared to the actual inclusion list of the reviews. Results: The best screening threshold rule identified studies that mentioned both exposure (E) and outcome (O) in the study abstract. This screening rule excluded 93.7% of retrieved studies with a recall of 98%. Conclusions: Filtering studies for inclusion in a systematic review based on the detection of key study characteristics in abstracts significantly outperformed standard approaches to automated screening and appears worthy of further development and evaluation. Twit Text FOAVip Automated screening of research studies for systematic reviews using study characteristics ????Syst Rev http://www.kidney.de/pget.php?&tw=1&pmid=29695296 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29695296 #FOAvip English Wikipedia <ref>{{Cite pmid|29695296}}</ref> Automated screening of research studies for systematic reviews using study characteristics #MMPMID29695296 Tsafnat G; Glasziou P; Karystianis G; Coiera E Syst Rev 2018[]; 7 (ä): ä PMID29695296show ga Background: Screening candidate studies for inclusion in a systematic review is time-consuming when conducted manually. Automation tools could reduce the human effort devoted to screening. Existing methods use supervised machine learning which train classifiers to identify relevant words in the abstracts of candidate articles that have previously been labelled by a human reviewer for inclusion or exclusion. Such classifiers typically reduce the number of abstracts requiring manual screening by about 50%. Methods: We extracted four key characteristics of observational studies (population, exposure, confounders and outcomes) from the text of titles and abstracts for all articles retrieved using search strategies from systematic reviews. Our screening method excluded studies if they did not meet a predefined set of characteristics. The method was evaluated using three systematic reviews. Screening results were compared to the actual inclusion list of the reviews. Results: The best screening threshold rule identified studies that mentioned both exposure (E) and outcome (O) in the study abstract. This screening rule excluded 93.7% of retrieved studies with a recall of 98%. Conclusions: Filtering studies for inclusion in a systematic review based on the detection of key study characteristics in abstracts significantly outperformed standard approaches to automated screening and appears worthy of further development and evaluation. äAutomated screening of research studies for systematic reviews using s(epmc).pdf DeepDyve Pubget Overpricing