Mapping Translational Bottlenecks in Periprosthetic Osteolysis Research: A Bibliometric Correlation Analysis

Introduction: Periprosthetic osteolysis (PPO) remains the leading cause of late-term failure in total joint arthroplasty. While bibliometric studies have mapped research trends, they have not systematically quantified the translational efficiency of different research themes toward reducing revision rates and prolonging implant survival. This study aimed to map the evolving research trends and identify translational bottlenecks in PPO research (2000–2025) by constructing a novel “clinical translational funnel” model, which quantifies the association strength between research themes and key clinical endpoints via an innovative keyword correlation analysis. Methods We retrieved 3,858 publications from the Web of Science Core Collection. VOSviewer and CiteSpace were used for co-occurrence, burst detection, timeline, and collaboration analyses. A novel keyword correlation strength calculation method was developed to categorize keywords into four translational tiers and quantify their associative strength with “revision” and “implant survival.” Results The analysis revealed a clear paradigm shift from basic pathology (2000–2010) to clinical risk management (2018–2025). Associative strength with “implant survival” attenuated sharply from the clinical/technical tier (e.g., “fixation”: strength = 35) to near-zero in molecular tiers. Key translational bottlenecks were identified, including the “titanium particle paradox” (high experimental use but low clinical relevance) and Western-centric collaboration patterns. Conclusion PPO management has transitioned toward a preventive, engineering-driven paradigm. Optimizing prosthetic fixation and wear-resistant materials represents the most efficient pathway to prolong implant survival. The proposed “translational funnel” model and keyword correlation method offer a data-driven framework for prioritizing clinically impactful research and reallocating resources to overcome identified bottlenecks.