Connecting the Dots: A Semantic Network Approach to Pediatric <scp>iNO</scp> Research Discovery

ABSTRACT In pediatric cardiac surgery, inhaled nitric oxide (iNO) is commonly used in managing postoperative pulmonary hypertension. However, a comprehensive understanding of its research trajectory remains incomplete. This study addresses that gap by applying advanced computational bibliometric techniques, including lexical network analysis, Latent Dirichlet Allocation (LDA) based topic modeling, Concept Level Entity Extraction (CLEE), and supergraph based semantic mapping, to elucidate the conceptual evolution of iNO research within the clinical domain. We examined 2517 publications sourced from Scopus, Web of Science, and PubMed. A two‐stage deduplication process, initially automated and subsequently refined through manual screening, yielded a curated dataset of 219 unique records. Scopus accounted for approximately 75% of all retrieved entries, a predominance consistent with its broader coverage of pediatric cardiac and perioperative literature, while funding analyses identified China as the primary driver of contemporary iNO scholarship. Lexical co‐occurrence networks revealed stable semantic clusters that remained coherent across robustness checks, and the integrated supergraph captured thematic transitions spanning six historical phases, ranging from early mechanistic explorations to current emphases on patient‐centered outcomes, perioperative optimization and translational readiness. These patterns may help identify where coordinated multicenter research could be most impactful, particularly regarding protocol harmonization, biomarker development and standardized reporting. This work constitutes the first reproducible historical reconstruction of pediatric iNO research using a supergraph based bibliometric framework, providing a descriptive synthesis that highlights knowledge gaps rather than inferring causality, and may support the design of future hypothesis‐driven translational investigations. This article is categorized under: Algorithmic Development &gt; Statistics Application Areas &gt; Science and Technology Technologies &gt; Machine Learning