Toward assessing clinical trial publications for reporting transparency

• DOI: 10.1016/j.jbi.2021.103717
• Publication Date: 2021-02-26T10:24:10Z
• Authors: Halil Kilicoglu; Graciela Rosemblat; Linh Hoang; Sahil Wadhwa; Zeshan Peng; Mario Malički; Jodi Schneider; Gerben ter Riet
• Keywords: 0301 basic medicine; 03 medical and health sciences; Support Vector Machine; 0302 clinical medicine; Serial Publications; Humans; Checklist; Randomized Controlled Trials as Topic

AbstractObjectiveTo annotate a corpus of randomized controlled trial (RCT) publications with the checklist items of CONSORT reporting guidelines and using the corpus to develop text mining methods for RCT appraisal.MethodsWe annotated a corpus of 50 RCT articles at the sentence level using 37 fine-grained CONSORT checklist items. A subset (31 articles) was double-annotated and adjudicated, while 19 were annotated by a single annotator and reconciled by another. We calculated inter-annotator agreement at the article and section level using MASI (Measuring Agreement on Set-Valued Items) and at the CONSORT item level using Krippendorff’s α. We experimented with two rule-based methods (phrase-based and section header-based) and two supervised learning approaches (support vector machine and BioBERT-based neural network classifiers), for recognizing 17 methodology-related items in the RCT Methods sections.ResultsWe created CONSORT-TM consisting of 10,709 sentences, 4,845 (45%) of which were annotated with 5,246 labels. A median of 28 CONSORT items (out of possible 37) were annotated per article. Agreement was moderate at the article and section levels (average MASI: 0.60 and 0.64, respectively). Agreement varied considerably among individual checklist items (Krippendorff’s α= 0.06-0.96). The model based on BioBERT performed best overall for recognizing methodology-related items (micro-precision: 0.82, micro-recall: 0.63, micro-F1: 0.71). Combining models using majority vote and label aggregation further improved precision and recall, respectively.ConclusionOur annotated corpus, CONSORT-TM, contains more fine-grained information than earlier RCT corpora. Low frequency of some CONSORT items made it difficult to train effective text mining models to recognize them. For the items commonly reported, CONSORT-TM can serve as a testbed for text mining methods that assess RCT transparency, rigor, and reliability, and support methods for peer review and authoring assistance. Minor modifications to the annotation scheme and a larger corpus could facilitate improved text mining models. CONSORT-TM is publicly available at https://github.com/kilicogluh/CONSORT-TM.Graphical abstractHighlightsWe constructed a corpus of RCT publications annotated with CONSORT checklist items.We developed text mining methods to identify methodology-related check-list items.A BioBERT-based model performs best in recognizing adequately reported items.A phrase-based method performs best in recognizing infrequently reported items.The corpus and the text mining methods can be used to address reporting transparency.