Goals and strategies for the indexing of publication types and study designs

Major research and implementation efforts have been devoted to indexing articles according to the major topics discussed, but much less effort to indexing their publication types and study designs (collectively, PTs). In this Perspective, we discuss how indexing PTs differs from topical Medical Subject Heading (MeSH) indexing and requires a different approach. Rather than focus on the technical aspects of machine learning-based indexing models, we emphasize the goals and purposes for which biomedical articles are indexed, and the surprisingly thorny question of how indexing systems should be evaluated. Topical Medical Subject Heading (MeSH) terms are assigned to articles that cover the major topics discussed; when more than one term is applicable, only the most specific term is assigned. In contrast, PTs are assigned to articles that have a given structure or use a particular design. To meet the needs of end users, particularly groups involved in evidence syntheses, PT indexing needs to be comprehensive and employ probabilistic goodness-of-fit prediction scores. Whereas existing National Library of Medicine (NLM) hierarchies place publication types and study design-related terms on separate trees from each other, we have created a unified hierarchy that permits more appropriate retrieval via automatic expansion. Automated PT indexing systems should allow users to input article records or full-text PDFs and receive scores in real time. This will offer consistent indexing across bibliographic databases, as well as preprints and unpublished manuscripts. Automated PT indexing systems, properly designed and implemented, hold the promise of greatly improving the retrieval of biomedical articles, saving substantial effort when writing evidence syntheses and benefiting other users as well. Database URL:  https://www.nlm.nih.gov/medline/medline_home.html.