A similarity metric, rubric, and unified hierarchy for biomedical publication types and study designs

Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine

Abstract Our goal is to unify the 72 biomedical publication types and study designs (collectively, PTs) into a single rubric and hierarchy. This is carried out in a data-driven manner by computing pairwise similarities of each PT against all others to form a similarity matrix. By performing hierarchical clustering we place each PT in a specific category and collect these into broader categories. Spearman correlations among PT pairs ranged from strongly negative to strongly positive (−0.732 to +0.997), with a mean of 0.176. Overall, we obtained 13 clusters of PTs and 5 more general categories: Observational Clinical Research, Qualitative and Genetic Methods, Clinical Evaluation and Validation, Interventional Trial Research, and Scholarly Synthesis and Discourse. These were then utilized to construct a unified hierarchy of PT terms. The rubric provides a flexible classification scheme for publication types and study designs that can accommodate new PTs as they are added over time. The similarity metric has the potential to improve the modelling, implementation, and evaluation of automated indexing systems. The PT rubric provides an overview that complements the existing NIH MeSH Hierarchy trees, and the unified hierarchy permits proper automated expansion for PT indexing and PubMed user queries involving PT terms.