Trained immunity in inflammatory bone disease: a bibliometric and literature-level text-mining analysis

BackgroundTrained immunity provides a framework for understanding persistent innate immune reprogramming in chronic inflammatory and immune-mediated disorders. Its relationship with osteoimmunology and inflammatory bone disease, however, remains insufficiently characterized.MethodsWe performed a bibliometric and literature-level text-mining analysis using WoSCC as the primary source for formal bibliometric analyses.

A two-corpus design was used, with a core corpus for disease-focused bibliometric analysis and an extended corpus for semantic screening and sensitivity analyses. Scopus and PubMed were queried independently for cross-database comparison.

Topic modeling, TF-IDF-based semantic screening, disease-context analysis, and bridge-network mapping were used to summarize literature-level patterns.ResultsThe core corpus included 83 records, and the extended corpus included 301 records. Within the full-year 2013–2025 comparison window, publication output increased over time and reached its highest level in 2025.

Cross-database comparison showed broadly concordant publication trends and substantial DOI-level overlap across WoSCC, Scopus, and PubMed. Periodontitis was the most prominent disease context, whereas osteoarthritis, ankylosing spondylitis, osteoporosis, and inflammatory arthritis also appeared in the disease-context mapping.