ArticleDatabase : the journal of biological databases and curation2026
A similarity metric, rubric, and unified hierarchy for biomedical publication types and study designs.
Article in Database : the journal of biological databases and curation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A similarity metric, rubric, and unified hierarchy for biomedical publication types and study designs.Database : the journal of biological databases and curation · 2026Article
- Goals and strategies for the indexing of publication types and study designs.Database : the journal of biological databases and curation · 2026Article
Corrections and comments
- Update of
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.