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10.1016/j.ymeth.2014.11.006 http://scihub22266oqcxt.onion/10.1016/j.ymeth.2014.11.006 C4339517!4339517 !25461812     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25461812 &cmd=llinks): Failed to open stream: HTTP request failed! 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Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial |pdf=|usr=}}{{25461812 }} gab.com Text Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial JO: Methods http://www.kidney.de/pget.php?&tw=1&pmid=25461812 #FOAvip OBJECTIVE: It is important to identify separate publications that report outcomes from the same underlying clinical trial, in order to avoid over-counting these as independent pieces of evidence. METHODS: We created positive and negative training sets (comprised of pairs of articles reporting on the same condition and intervention) that were, or were not, linked to the same clinicaltrials.gov trial registry number. Features were extracted from MEDLINE and PubMed metadata; pairwise similarity scores were modeled using logistic regression. RESULTS: Article pairs from the same trial were identified with high accuracy (F1 score=0.843). We also created a clustering tool, Aggregator, that takes as input a PubMed user query for RCTs on a given topic, and returns article clusters predicted to arise from the same clinical trial. DISCUSSION: Although painstaking examination of full-text may be needed to be conclusive, metadata are surprisingly accurate in predicting when two articles derive from the same underlying clinical trial. Twit Text FOAVip Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial ????Methods http://www.kidney.de/pget.php?&tw=1&pmid=25461812 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25461812 #FOAvip English Wikipedia <ref>{{Cite pmid|25461812 }}</ref> Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial #MMPMID25461812 Shao W ; Adams CE ; Cohen AM ; Davis JM ; McDonagh MS ; Thakurta S ; Yu PS ; Smalheiser NR Methods 2015[Mar]; 74 (ä): 65-70 PMID25461812 show ga OBJECTIVE: It is important to identify separate publications that report outcomes from the same underlying clinical trial, in order to avoid over-counting these as independent pieces of evidence. METHODS: We created positive and negative training sets (comprised of pairs of articles reporting on the same condition and intervention) that were, or were not, linked to the same clinicaltrials.gov trial registry number. Features were extracted from MEDLINE and PubMed metadata; pairwise similarity scores were modeled using logistic regression. RESULTS: Article pairs from the same trial were identified with high accuracy (F1 score=0.843). We also created a clustering tool, Aggregator, that takes as input a PubMed user query for RCTs on a given topic, and returns article clusters predicted to arise from the same clinical trial. DISCUSSION: Although painstaking examination of full-text may be needed to be conclusive, metadata are surprisingly accurate in predicting when two articles derive from the same underlying clinical trial. |*Machine Learning [MESH] |Clinical Trials as Topic/*statistics & numerical data [MESH] |Cluster Analysis [MESH] |Humans [MESH]Aggregator: a machine learning approach to identifying MEDLINE article(epmc).pdf DeepDyve Pubget Overpricing