Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial
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 o...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Elsevier
2015
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/46915/ |
| _version_ | 1848797426753732608 |
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| author | Shao, Weixiang Adams, Clive E. Cohen, Aaron M. Davis, John M. McDonagh, Marian S. Thakurta, Sujata Yu, Philip S. Smalheiser, Neil R. |
| author_facet | Shao, Weixiang Adams, Clive E. Cohen, Aaron M. Davis, John M. McDonagh, Marian S. Thakurta, Sujata Yu, Philip S. Smalheiser, Neil R. |
| author_sort | Shao, Weixiang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:03:42Z |
| format | Article |
| id | nottingham-46915 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:03:42Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-469152020-05-04T17:01:44Z https://eprints.nottingham.ac.uk/46915/ Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial Shao, Weixiang Adams, Clive E. Cohen, Aaron M. Davis, John M. McDonagh, Marian S. Thakurta, Sujata Yu, Philip S. Smalheiser, Neil R. 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. Elsevier 2015-03-01 Article PeerReviewed Shao, Weixiang, Adams, Clive E., Cohen, Aaron M., Davis, John M., McDonagh, Marian S., Thakurta, Sujata, Yu, Philip S. and Smalheiser, Neil R. (2015) Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial. Methods, 74 . pp. 65-70. ISSN 1095-9130 Evidence-based medicine; Clinical trials; Systematic reviews; Bias; Information retrieval; Informatics http://www.sciencedirect.com/science/article/pii/S1046202314003661 doi:10.1016/j.ymeth.2014.11.006 doi:10.1016/j.ymeth.2014.11.006 |
| spellingShingle | Evidence-based medicine; Clinical trials; Systematic reviews; Bias; Information retrieval; Informatics Shao, Weixiang Adams, Clive E. Cohen, Aaron M. Davis, John M. McDonagh, Marian S. Thakurta, Sujata Yu, Philip S. Smalheiser, Neil R. Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial |
| title | Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial |
| title_full | Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial |
| title_fullStr | Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial |
| title_full_unstemmed | Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial |
| title_short | Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial |
| title_sort | aggregator: a machine learning approach to identifying medline articles that derive from the same underlying clinical trial |
| topic | Evidence-based medicine; Clinical trials; Systematic reviews; Bias; Information retrieval; Informatics |
| url | https://eprints.nottingham.ac.uk/46915/ https://eprints.nottingham.ac.uk/46915/ https://eprints.nottingham.ac.uk/46915/ |