Identifying Unreported Links between ClinicalTrials.gov Trial Registrations and Their Published Results
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| Title: | Identifying Unreported Links between ClinicalTrials.gov Trial Registrations and Their Published Results |
|---|---|
| Language: | English |
| Authors: | Liu, Shifeng, Bourgeois, Florence T., Dunn, Adam G. (ORCID |
| Source: | Research Synthesis Methods. May 2022 13(3):342-352. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2022 |
| Sponsoring Agency: | National Library of Medicine (DHHS/NIH) |
| Contract Number: | R01LM012976 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Medical Research, Web Sites, Identification, Computational Linguistics, Comparative Analysis, Publications, Research Reports, Prediction, Drug Therapy, Users (Information), Databases |
| DOI: | 10.1002/jrsm.1545 |
| ISSN: | 1759-2879 |
| Abstract: | A substantial proportion of trial registrations are not linked to corresponding published articles, limiting analyses and new tools. Our aim was to develop a method for finding articles reporting the results of trials that are registered on ClinicalTrials.gov when they do not include metadata links. We used a set of 27,280 trial registration and article pairs to train and evaluate methods for identifying missing links in both directions--from articles to registrations and from registrations to articles. We trained a classifier with six distance metrics as feature representations to rank the correct article or registration, using recall@K to evaluate performance and compare to baseline methods. When identifying links from registrations to published articles, the classifier ranked the correct article first (recall@1) among 378,048 articles in 80.8% of evaluation cases and 34.9% in the baseline method. Recall@10 was 85.1% compared to 60.7% in the baseline. When predicting links from articles to registrations, recall@1 was 83.4% for the classifier and 39.8% in the baseline. Recall@10 was 89.5% compared to 65.8% in the baseline. The proposed method improves on our baseline document similarity method to be feasible for identifying missing links in practice. Given a ClinicalTrials.gov registration, a user checking 10 ranked articles can expect to identify the matching article in at least 85% of cases, if the trial has been published. The proposed method can be used to improve the coupling of ClinicalTrials.gov and PubMed, with applications related to automating systematic review and evidence synthesis processes. |
| Abstractor: | As Provided |
| Notes: | https://github.com/evidence-surveillance/unreported_link_identidication |
| Entry Date: | 2022 |
| Accession Number: | EJ1335011 |
| Database: | ERIC |
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