Cited by 36Meta-analysis and systematic reviewsBiomedical Text Mining and OntologiesComputational and Text Analysis Methods

Toward assessing clinical trial publications for reporting transparency

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Kilicoglu, Halil, Rosemblat, Graciela, Hoang, Linh, Wadhwa, Sahil, Peng, Zeshan, Malički, Mario, Schneider, Jodi, ter Riet, Gerben·2021·Journal of Biomedical Informatics

Published version of a preprint

Abstract

Objective To 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.Methods We 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.Results We 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.Conclusion Our 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.

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Authors
Kilicoglu, H., Rosemblat, G., Hoang, L., Wadhwa, S., Peng, Z., Malički, M., Schneider, J., & ter Riet, G.
Affiliations
  • National Institutes of Health (US)
  • University of Illinois Urbana-Champaign (US)
  • Stanford University (US)
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2021-04
Volume
116
Pages
103717
ISSN
1532-0464 (ISSN-L)
OpenAlex
W3133622905
Cited by
34
References
66
Citations by year
2026: 3 · 2025: 12 · 2024: 10 · 2023: 4 · 2022: 4 · 2021: 1
FWCI
2.75
Type
journal article
Language
en
Open access
Locations
  • Journal of Biomedical Informatics (publishedVersion)
  • medRxiv (acceptedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
  • Pure Amsterdam UMC (submittedVersion, cc-by)
Funders
National Library of Medicine
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
randomized controlled trials, reporting transparency, annotated corpus, text mining, inter-annotator agreement, BioBERT, sentence-level annotation, peer review
MeSH terms
Humans, Serial Publications, Randomized Controlled Trials as Topic, Checklist, Support Vector Machine

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