Adam G Dunn1, Enrico Coiera2, Florence T Bourgeois3. 1. Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney 2109, New South Wales, Australia. Electronic address: adam.dunn@mq.edu.au. 2. Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney 2109, New South Wales, Australia. 3. Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
Abstract
OBJECTIVES: Trial registries can be used to measure reporting biases and support systematic reviews, but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. STUDY DESIGN AND SETTING: We extracted terms and concepts from a data set of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order. RESULTS: The best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1-21) for reported links and 3 (IQR 1-19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1-20) for reported links and 2 (IQR 1-12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links. CONCLUSION: Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.
OBJECTIVES: Trial registries can be used to measure reporting biases and support systematic reviews, but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. STUDY DESIGN AND SETTING: We extracted terms and concepts from a data set of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order. RESULTS: The best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1-21) for reported links and 3 (IQR 1-19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1-20) for reported links and 2 (IQR 1-12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links. CONCLUSION: Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.
Authors: Stuart G Nicholls; Steve McDonald; Joanne E McKenzie; Kelly Carroll; Monica Taljaard Journal: J Clin Epidemiol Date: 2022-01-23 Impact factor: 7.407