4D Bioassembly: Evolutionary Pathway and Future of Osteochondral Repair via Bibliometric Analysis

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While bibliometrics can map this expansion, a deeper synthesis is needed to uncover the underlying dynamics and future paradigms that will strategically guide the field. METHODS A systematic search of the Web of Science Core Collection (2000–2024) identified 2,919 publications for analysis. VOSviewer (v1.6.20) constructed co-occurrence and co-citation networks, while CiteSpace (v6.4.R1) was employed for burst detection, dual-map overlays, collaboration analysis, and timezone views to reveal evolution pathways. RESULTS Our analysis reveals a mature, tripartite intellectual structure and a robust bidirectional knowledge flow, forming a “translational closed-loop.” We document significant global research asymmetry and conceptualize the field’s evolution as three sequential paradigms: 1) Structural Repair (c. 2000–2010); 2) Biological Regeneration (c. 2011–2017); and 3) Functional Mimicry (2018–present). Critically, our data identify the convergence of sustained citation bursts in “osteochondral regeneration” (strength: 19.93), “3D printing,” and “hydrogel” as the empirical foundation for a new, emerging paradigm: 4D Bioassembly (spatiotemporally programmed and self-evolving regeneration of living tissues). CONCLUSION We conclude that the field’s trajectory is defined by its progression into the Functional Mimicry paradigm and is now pivoting toward the ultimate frontier of 4D Bioassembly. Biomedical Engineering Bibliometrics Osteochondral Repair Tissue Engineering 3D Printing Regenerative Medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Articular cartilage injury is a prevalent orthopedic condition, arising from causes such as overuse, trauma, and degeneration [ 1 – 3 ], yet its repair is severely constrained by the tissue’s avascular, alymphatic, and aneural nature [ 4 ]. The progressive involvement of the subchondral bone leads to osteochondral defect formation [ 5 – 7 ], subsequently aggravating joint dysfunction and pain while substantially compromising patient quality of life [ 8 ]. Currently, standard clinical interventions for articular cartilage repair primarily comprise microfracture (MF), autologous or allogeneic osteochondral transplantation (OCT), and autologous chondrocyte implantation (ACI) [ 9 , 10 ]. However, these approaches present significant limitations. For instance, MF often results in the formation of fibrocartilage, which exhibits inferior biomechanical properties compared with native hyaline cartilage [ 11 ]. Other limitations include poor integration with the host tissue and long-term degeneration post-transplantation [ 12 , 13 ], as well as the uncertain long-term efficacy of ACI technology [ 14 – 16 ]. Consequently, there is a pressing clinical need to develop novel therapeutic strategies capable of achieving both biological and functional restoration. Since the 1990s, osteochondral repair strategies have undergone significant evolution. The initial stage focused on marrow stimulation techniques, such as MF, which aimed to promote defect filling by triggering a repair response or implanting active cells [ 17 – 19 ]. Subsequently, osteochondral transplantation techniques were developed; however, their application has been constrained by limitations in donor availability, risks of immune rejection, and potential disease transmission [ 20 – 22 ]. The recent emergence of tissue engineering has provided novel directions for osteochondral regeneration. By integrating three-dimensional porous scaffolds, bioactive factors, and stem cell technology, tissue engineering strategies aim to biomimetically construct osteochondral composite tissues with gradient structures and biological functions, thereby achieving more effective and durable repair outcomes [ 23 – 25 ]. The rapid advancement in the field of osteochondral repair has been accompanied by an exponential growth in related scientific publications, encompassing diverse aspects such as material design, fabrication techniques, in vitro and in vivo evaluation, and clinical translation. Confronted with this vast and fragmented knowledge landscape, conventional review methodologies struggle to provide a comprehensive and objective perspective on the field’s overall developmental trajectory, collaborative network structures, and thematic evolution. Bibliometrics, as a quantitative analysis method for scientific literature, offers the capability to reveal latent research trends, identify core authors and institutional collaboration patterns, and detect emerging frontiers within the discipline [ 26 , 27 ]. Through a systematic analysis of relevant literature published from 2000 to 2024, this study aims to construct a knowledge map of the osteochondral repair field. Building upon this foundation, we employ a multi-method bibliometric analysis not merely to describe, but to decode the field of osteochondral repair. We seek to transcend traditional description by pursuing four pivotal intellectual contributions: First, to move beyond listing hotspots to delineate the fundamental paradigms that have defined the field’s evolution. Second, to uncover the intrinsic mechanisms of knowledge translation, testing whether the field operates as a linear pipeline or a more dynamic, interactive system. Third, to diagnose the global collaborative anatomy, assessing not just who produces knowledge, but who truly connects and leads the global network. Finally, by synthesizing these insights, we aim to forecast strategic frontiers and define the next research paradigm. Through this approach, we seek to provide not just a map of the past, but a compass for the future, offering a data-driven roadmap for the next decade of innovation. Materials and Methods Data Sources and Search Strategy On July 30, 2025, a systematic retrieval was conducted utilizing the Web of Science Core Collection (WoSCC) database to identify literature on osteochondral repair published between January 1, 2000, and December 31, 2024. The search strategy was constructed based on the PICO framework, combining core concepts including “osteochondral defect/injur,” “repair/regenerat/tissue engineer,” and “hydrogel/scaffold/biomaterial” in the Topic (TS) and Title (TI) fields. The detailed search strategy is presented in Table 1 . Table 1 Search strategy in Web of Science Core Collection. Step Query Scope #1 TS=(“osteochondral defect” OR “osteochondral injury” OR “osteochondral lesion”) Topic #2 TS=(“microfracture” OR “autologous chondrocyte implantation” OR “ACI” OR “osteochondral autograft transfer” OR “OATS” OR “matrix-induced autologous chondrocyte implantation” OR “MACI” OR “osteochondral allograft” OR regenerat OR scaffold* OR “tissue engineering” OR hydrogel OR “3D print*”) Topic #3 #1 AND #2 #4 NOT WC=(“Veterinary Sciences” OR “Dentistry Oral Surgery Medicine” OR “Oncology”) Category #5 #3 NOT #4 #6 #5 AND PY=(2000–2024) Year #7 #6 AND DT=(Article OR Review) Doc Type #8 #7 AND LA=(English) Language #9 Final result: #8 *TS: Topic field; WC: Web of Science category; PY: Publication year; DT: Document type; LA: Language. *Note: The literature time scope of this study is set to 2000–2024, and the final retrieval was conducted on July 30, 2025. This is to avoid the 1–3 month indexing lag of databases after journal publication, ensuring the complete inclusion of literatures published in 2024 and reducing the risk of missed retrieval. Literature Screening Process The literature screening was performed independently by two researchers according to the PRISMA guidelines. Titles and abstracts of the initially identified 2,934 records were screened against predefined eligibility criteria. Inclusion Criteria were: (1) studies focusing on the repair or regeneration of osteochondral or articular cartilage defects; (2) investigations involving tissue engineering or regenerative medicine strategies (e.g., scaffolds, hydrogels, 3D printing, cell therapy); (3) original research articles (in vitro, in vivo, or clinical) or systematic reviews; (4) publications dated between 2000 and 2024; and (5) English-language publications. Exclusion Criteria included: (1) studies irrelevant to osteochondral repair; (2) research on non-articular cartilage or non-orthopedic fields; (3) studies focusing on periprosthetic osteolysis, infectious arthritis, or conservative osteoarthritis management; (4) non-peer-reviewed publications; and (5) articles with unavailable full text. Through this process, 15 records were excluded (3 retracted articles and 12 irrelevant publications), resulting in 2,919 publications for final analysis. Analytical Methods and Tools Bibliographic records exported from WoSCC constituted the primary dataset. A multi-method analytical framework was employed: Descriptive Analysis : Fundamental characteristics (e.g., annual output, journal distribution) were quantified using Microsoft Excel. Science Mapping : VOSviewer was used to construct and visualize co-occurrence and co-citation networks, with cluster analysis identifying research themes. Evolutionary and Frontier Analysis : CiteSpace was employed for keyword burst detection, timeline visualization, dual-map overlay analysis, and collaboration network analysis to uncover trends and frontiers. This integrated framework was designed to elucidate the knowledge structure and developmental trajectory of osteochondral repair research from macro- to micro-level perspectives. Results Annual Publication Trends, Citation Impact, and Disciplinary Distribution Analysis of Literature Collection and Annual Trends This study was based on the Web of Science Core Collection database (SCI-EXPANDED), through which a systematic retrieval of research literature on osteochondral repair published between 2000 and 2024 was conducted. Following a rigorous screening process, a total of 2,919 academic publications meeting the predefined criteria were ultimately included, establishing the baseline dataset for this bibliometric analysis. Time-series analysis of this dataset revealed distinct developmental dynamics within the field (Fig. 1 A). During the initial stage (2000–2004), annual output remained low (average: 29.4 publications/year), reflecting a phase of technological exploration. The field then entered a period of rapid growth (2005–2013), with annual output surging to an average of 95.4 publications and exceeding 100 for the first time in 2013. From 2014 to 2020, research entered a stable developmental plateau, maintaining a high annual output (average: 159.1 publications), which confirms its establishment as a mature subfield. Publication output peaked in 2021 (220 publications). The sustained high output in the most recent years (2022–2023) robustly confirms the field’s continued vitality and innovative potential. Analysis of Citation Impact Citation analysis underscores the substantial impact of osteochondral repair research (Fig. 1 B). The 2,919 publications received 127,588 total citations, with 101,804 non-self-citations, demonstrating broad academic resonance. The field exhibits a high average of 43.71 citations per article and an H-index of 152, collectively affirming the high impact and quality of its research output. A Publication volume. The annual number of publications shows a fluctuating upward trend, with a peak in 2021. B Citation metrics. The blue line depicts the annual number of publications. The red line shows the cumulative number of citations received. Disciplinary Distribution and Interdisciplinary Characteristics Analysis of disciplinary co-occurrence confirms the interdisciplinary nature of osteochondral repair research, with publications spanning over 80 Web of Science categories. Orthopedics formed the dominant core (1,349 publications, 46.21%), establishing the clinical foundation. A robust core research cluster included Biomedical Engineering (22.71%), Sport Sciences (19.80%), Materials Science Biomaterials (19.53%), and Surgery (17.85%), driving advancements in materials, biomechanics, and clinical translation. The significant presence of Cell & Tissue Engineering (10.93%) and Cell Biology (8.36%) highlights regenerative medicine as a key frontier. The sum of percentages exceeding 100% (Fig. 2 ) directly evidences the frequent cross-disciplinary classification of publications in this field. Frequency of research area assignments (Total assignments: 10,419 from 2,919 publications). Red curve: Field Proportion (%). Crossover index = 357.01% (Total assignments/Total publications). Analysis of National and Institutional Collaboration Networks Collaboration networks among countries and institutions were mapped using CiteSpace to assess the global research landscape (2000–2023). Analysis of National Collaboration Networks The United States led in both publication output (819) and betweenness centrality (0.55), positioning it as the central hub of the global network (Table 2 , Fig. 3 A). China ranked second in output (548) but had low centrality (0.11). Several European countries, including Germany, Italy, England, and the Netherlands, also showed significant centrality, acting as important connectors. Visually, the USA and key European nations form the network core with dense connections, whereas China, despite its high output, is peripherally positioned with fewer and weaker links. Table 2 Top 10 productive countries and their centrality metrics. Rank Country Count Centrality Begin Year 1 USA 819 0.55 2000 2 China 548 0.11 2004 3 Germany 344 0.12 2000 4 Italy 254 0.16 2000 5 Japan 242 0.08 2001 6 England 198 0.14 2002 7 South Korea 141 0.08 2000 8 Switzerland 132 0.07 2002 9 Netherlands 123 0.14 2004 10 Canada 80 0.05 2001 *Note: Betweenness centrality measures a country’s role as a connector in the global collaboration network. Higher values indicate greater importance as a bridge. Analysis of Institutional Collaboration Networks IRCCS Istituto Ortopedico Rizzoli (88 publications) and the University of London (81) were the most productive institutions (Fig. 3 B). The University of California system held the highest centrality (0.12), indicating its pivotal bridging role. While several Chinese institutions, namely Shanghai Jiao Tong University (5th), Chinese Academy of Sciences (8th), Peking University (9th), and Zhejiang University (10th), ranked among the top 10 in productivity (as shown in Table 3), all exhibited low centrality (below 0.05). The network core is composed of the University of California system, Harvard University and its affiliates, and leading European institutions, with Chinese institutions predominantly in non-core positions. A Country-level network. Network of international co-authorships (2000–2024). Node size: publication count; line thickness: collaboration strength; node color (red-higher): betweenness centrality. B Institutional network (2000–2024). Network visualization of inter-institutional co-authorship patterns. Node size corresponds to publication output; line thickness indicates collaboration strength; node color reflects betweenness centrality (warmer hues indicate higher values). The network structure reveals distinct collaborative clusters with the University of California System occupying the most central position. The collaboration network metrics reveal a distinct core-periphery architecture, with the United States as the central hub. The marked discrepancy between China’s publication output and its betweenness centrality delineates a quantifiable asymmetry in global collaborative influence. Analysis of Author Collaboration Networks The author collaboration network, constructed using VOSviewer, comprised 49 authors forming 7 major clusters (Fig. 4 ). Italian researchers Filardo, Giuseppe and Kon, Elizaveta occupied the most central positions as international collaboration hubs, whereas the team led by American scholar Kennedy, John G. had the highest publication count (42) but a more concentrated collaborative scope. Network analysis identified four primary clusters: an Italian cluster focused on clinical translation; a North American cluster specializing in sports medicine; a multinational European cluster concentrating on basic research and tissue engineering; and a Japanese cluster emphasizing biomaterials and implant development. This regionally and thematically specialized architecture substantiates the field’s tripartite intellectual structure. Co-authorship network of 49 authors. Node size corresponds to publication count; line thickness represents collaboration strength. Nodes are colored based on cluster analysis. Intellectual Base: Document Co-citation and Journal Co-citation Analysis Analysis of Document Co-citation Networks The intellectual structure of the osteochondral repair research field is organized into three major clusters (Fig. 5 A): Cluster 1 (Red): This cluster is centered around foundational publications such as Knutsen G (2004), with its constituent literature primarily focusing on randomized controlled trials and long-term efficacy evaluation; Cluster 2 (Green): This cluster is anchored by seminal works including Brittberg M (1994), encompassing literature that predominantly addresses chondral biological mechanisms, cell-based therapies, and tissue regeneration; Cluster 3 (Blue): This cluster features pivotal studies such as Chuckpaiwong B (2008), with its associated publications mainly concentrating on surgical technique applications, clinical outcomes, and complication analysis. Analysis of Journal Co-citation Network The intellectual structure of the field was further deconstructed through journal co-citation analysis (Fig. 5 B). The cluster anchored by The American Journal of Sports Medicine (green nodes) corresponds to the clinical orthopedics and sports medicine domain, emphasizing surgical techniques, treatment outcome evaluation, and optimization of clinical efficacy; the cluster featuring Journal of Orthopaedic Research (blue nodes) reflects the basic science and bone biology orientation, dedicated to fundamental research such as mechanisms of cartilage degeneration, cell differentiation, and regenerative processes. The strong co-citation relationships among these clusters highlight the highly interdisciplinary nature of the field, whose advancement relies on the deep integration and synergistic interaction among material technology innovation, clinically driven problem-solving, and fundamental scientific discovery. A Document co-citation. Network visualization of co-cited references generated using VOSviewer. Node size corresponds to citation frequency. Colors indicate distinct thematic clusters: red (Cluster 1), green (Cluster 2), and blue (Cluster 3). Lines represent co-citation relationships between references. B Journal co-citation. This figure was generated through journal co-citation analysis. Node size represents the frequency of journal co-citation, while line thickness indicates the strength of co-citation relationships. Node colors, automatically generated based on cluster analysis, identify three major research clusters. Interdisciplinary Mapping: Dual-map Overlay The dual-map overlay clearly delineates three primary knowledge-source disciplines and their cross-domain flow trajectories within osteochondral repair research (Fig. 6 ). The purple trajectory demonstrates substantial knowledge transfer from Physics/Materials/Chemistry (left #5) to applied Chemistry/Materials clusters (right #4; z = 2.74, f = 1641) and Molecular Biology/Genetics clusters (right #8; z = 2.38, f = 1456), indicating material science’s dual role in driving both biomaterial development and basic biological research. Simultaneously, the yellow trajectory reveals Molecular Biology/Immunology (left #4) as an interdisciplinary hub, disseminating knowledge to Chemistry/Materials (right #4; z = 1.70, f = 1108), Molecular Genetics (right #8; z = 3.95, f = 2256), and Sports/Rehabilitation clusters (right #9; z = 2.04, f = 1284), thereby bridging basic science and clinical applications. Most significantly, the gray trajectory exhibits a robust reverse knowledge flow from clinical disciplines—Neurology/Sports/Ophthalmology (left #8)—to all three major research frontiers (right #4: z = 2.06, f = 1291; right #8: z = 3.75, f = 2152; right #9: z = 3.32, f = 1984), highlighting a critical feedback mechanism wherein clinical practice actively informs and directs fundamental scientific exploration, ultimately reflecting the deeply integrated and bidirectional nature of knowledge exchange in this field. The combined evidence from co-citation and dual-map analyses reveals a systematically integrated knowledge architecture in osteochondral repair, defined by a tripartite intellectual structure connected through a bidirectional knowledge flow and, most crucially, a pervasive translational feedback loop. This loop is evidenced by a strong reverse citation trajectory from clinical disciplines to basic science, whereby clinical insights directly catalyze foundational and technological inquiry, which in turn cycles back to advance clinical practice. This dual-map overlay illustrates interdisciplinary knowledge exchange through citation trajectories. The width of each curve corresponds to the z-score (measuring the statistical strength of the connection), while the frequency (f) represents the raw count of citation instances. Source disciplines are shown on the left, and target research fronts are displayed on the right. Research Focus and Evolution: Analysis of Keyword Co-occurrence, Clustering, and Burst Detection To systematically elucidate the research hotspots, knowledge structure, and evolutionary trajectory in the field of osteochondral repair, this study employed both VOSviewer and CiteSpace for keyword co-occurrence and burst detection analyses. The results from these methods were integrated to provide a comprehensive understanding of the field’s thematic architecture and its dynamic shifts. Thematic Architecture from Co-occurrence Clustering Keyword co-occurrence analysis revealed the field’s thematic structure through two complementary visualizations. VOSviewer identified distinct color-coded clusters (Fig. 7 A), while CiteSpace delineated five major clusters (Fig. 7 B), both reflecting core research themes: Basic Science & Tissue Engineering (VOSviewer Red / CiteSpace #1) : Focused on mechanisms and strategies, with core keywords including mesenchymal stem cells, tissue engineering, and chondrogenic differentiation. Biomaterial Design & Regeneration (CiteSpace #2) : Characterized by a thematic focus on the fabrication of biomaterials for regenerative applications, with keywords such as bilayered scaffold, biphasic scaffold, and 3D printing. These keywords reflect a central research drive: engineering scaffolds with increasingly sophisticated and biomimetic structural complexity. Clinical Interventions (VOSviewer Green / CiteSpace #0) : Predominantly concerned with clinical techniques, encompassing keywords like transplantation, microfracture, and autologous chondrocyte implantation. Bioactive Factors (VOSviewer Blue) : Highlighting the therapeutic applications of bioactive factors, with terms including platelet-rich plasma, knee osteoarthritis, and hyaluronic acid. Pathology & Outcomes (CiteSpace #3, #4) : Complementing the structure by focusing on osteochondral defect/injury models and clinical outcome evaluation, respectively. A Co-occurrence network. Network generated using VOSviewer. Node size corresponds to keyword frequency; link thickness indicates co-occurrence strength. Colored clusters represent distinct research themes in the field. B Clustering map (2000–2024). The network was pruned using the Pathfinder algorithm. Modularity (Q = 0.4377) and weighted mean silhouette (S = 0.7019) indicate a significant and credible cluster structure. Five major clusters were identified and labeled using the log-likelihood ratio (LLR) algorithm. Temporal Evolution from Burst Detection and Timezone View Keyword burst detection analysis (Fig. 8 A) identifies research topics with sharp increases in citation frequency, while the timezone view (Fig. 8 B) visually displays their chronological emergence. Combined, these methods reveal a distinct temporal evolution: 2000–2010 (Structural Repair) : This phase was defined by keywords with the highest burst strength, including full-thickness defects (strength: 24.60), osteochondral defects (strength: 25.12), and autologous chondrocyte transplantation (strength: 7.40), indicating a primary focus on defect management and first-generation surgical techniques. 2011–2017 (Biological Regeneration) : The research focus shifted, marked by the emergence of keywords such as randomized trial (strength: 7.81) and the rise of scaffold, mesenchymal stem cells, and platelet-rich plasma. This signifies a transition towards evidence-based medicine and active regeneration using cells and bioactive factors. 2018–Present (Functional Mimicry) : The current frontier is authoritatively defined by the most powerful burst keyword, osteochondral regeneration (strength: 19.93), signaling a definitive shift in the field’s ambition from repair to true regeneration. This goal is operationally driven by the sustained citation bursts of key enabling technologies. The data reveal a powerful convergence: 3D printing (strength: 7.90) provides the capability for architectural precision, while hydrogel (strength: 8.18) introduces essential dynamic functionality. The co-emergence of these technologies signifies that the field’s focus has expanded from creating static structural mimics to engineering constructs capable of supporting more complex biological processes. The powerful convergence of the regenerative objective with the means of advanced fabrication and smart materials provides compelling empirical evidence that the field is evolving beyond the current Functional Mimicry paradigm. A Top 20 keywords with strongest citation bursts. Keywords are ranked by burst strength, which indicates a sharp increase in citation frequency within a specific period. The red line segment represents the duration of the burst. B Keyword timezone view. The visualization maps the emergence and evolution of research themes based on keyword co-occurrence. The horizontal axis represents publication years. Node size reflects keyword frequency, and links represent co-occurrence relationships between keywords. Discussion This convergence is not an endpoint; rather, it represents the active assembly of a technological foundation that is inherently dynamic and adaptive. We interpret this foundational shift as the empirical emergence of a new paradigm, which we conceptualize as 4D Bioassembly. This multidimensional bibliometric analysis delineates the maturation of osteochondral repair through three core mechanisms that explain this progression: the function of a “translational feedback loop,” the structure of “global research asymmetry,” and the trajectory of a “triple-paradigm evolution.” Together, these mechanisms not only decode the field’s past development but also provide a strategic framework for navigating its future trajectory toward 4D Bioassembly. Pivoting from Functional Mimicry toward 4D Bioassembly The temporal and thematic progression of keywords, when integrated, provides robust evidence for the field’s triple-paradigm evolution. The centrality of microfracture and transplantation in the early-stage clusters (e.g., Cluster #0, Green VOSviewer cluster) anchors the Structural Repair paradigm, as evidenced by influential clinical research of the era [ 28 ]. The subsequent rise of tissue engineering, mesenchymal stem cells, and scaffolds signals the shift to the Biological Regeneration paradigm. Finally, the emergence and dominance of osteochondral regeneration as a core cluster (#2), specifically defined by 3D printing and fabrication and confirmed by its high burst strength, clearly demarcates the current frontier of Functional Mimicry, focused on replicating the native tissue’s complex architecture. This established evolutionary pathway not only explains the past but also clearly signals the necessity to pivot toward the emerging paradigm of 4D Bioassembly (Fig. 9 ). The conceptual evolution of osteochondral repair has progressed through three sequential paradigms—Structural Repair, Biological Regeneration, and Functional Mimicry—and is now advancing toward the emerging frontier of 4D Bioassembly, which aims for spatiotemporally programmed, self-evolving regeneration. Evolution of the Intellectual Structure and Its Interdisciplinary Nature The field’s tripartite intellectual structure—integrating biomaterials and tissue engineering, clinical orthopedics and sports medicine, and basic bone and cartilage biology—functions not in isolation but as a synergistically evolving organic whole, forming a translational feedback loop. This integrative dynamic is evident throughout the field’s history. For instance, the clinical refinement of ACI, from demonstrating long-term efficacy [ 29 ] to addressing complex degenerative conditions [ 30 ], has been profoundly dependent on foundational biological discoveries. Seminal work on the mechanisms of cartilage repair [ 31 ] and cellular differentiation [ 32 ] provided the theoretical basis for such regenerative strategies, while advances in biomaterial design [ 33 ] offered the means to execute them. Conversely, clinical investigations actively guide fundamental research. Studies evaluating the limitations of microfracture technique highlighted unmet clinical needs, motivating the development of enhanced technologies [ 34 ]. Similarly, research elucidating the mechanism of action of platelet-rich plasma (PRP) established its biological rationale and paved the way for its broad application [ 35 ]. This evidence demonstrates that the field operates not as a linear pipeline but as a dynamic, integrated system—a translational feedback loop. This fundamental mechanism ensures that clinical challenges seed scientific inquiry, and technological breakthroughs are rapidly validated against real-world needs, creating a virtuous cycle that drives the field’s advancement. Global Research Asymmetry and Strategic Collaboration Beyond its integrated intellectual core, the field’s growth is shaped by a distinct global collaboration anatomy. Our analysis reveals a pronounced asymmetry between research output and collaborative influence on a global scale. The United States functions as the central hub, a position sustained by its robust research ecosystem, clinical resources, and ability to attract elite talent, as reflected in the high centrality of institutions like the University of California system and Harvard University. European nations form strong secondary clusters through dense intraregional collaboration, playing a critical “bridging” role in global knowledge flow. In contrast, several high-output countries exhibit relatively low betweenness centrality, indicating that their institutions act more as “major contributors” than “global hubs.” This disparity may be influenced by historical development trajectories, international collaboration patterns, and linguistic or cultural factors. Therefore, this global research asymmetry represents a critical strategic bottleneck. For high-output yet peripheral countries, future progress hinges on fostering strategic partnerships with core international teams and shifting the emphasis from quantitative output to enhancing pivotal roles within the global innovation network. Bridging this “centrality gap” is imperative for unlocking a more equitable and efficient research ecosystem, ultimately transitioning key players from the role of contributors to that of architectural leaders. The Forthcoming Frontier: From Functional Mimicry to 4D Bioassembly Having established the empirical impetus for a new paradigm from our bibliometric data, we now define and substantiate the concept of 4D Bioassembly. The empirical foundation for this shift is laid by the convergence of key enabling technologies. Foundational work on gene-activated matrices demonstrated the temporal programming of cellular fate [ 36 ]. Advances in nanomaterials enhanced the precision control over cellular microenvironments [ 37 ], while integrated 3D printing technologies enabled spatially orchestrated tissue formation [ 38 ]. Most notably, smart biomaterials such as the aggrecanase-1-responsive hydrogel developed by Feng et al. exemplify systems capable of dynamic interaction with the pathological microenvironment [ 39 ]. This technological convergence points unequivocally to a field outgrowing its current paradigm. While Functional Mimicry aims to recapitulate the native tissue’s complex structure at a given timepoint, the next logical and data-supported frontier is 4D Bioassembly—the spatiotemporally programmed and self-evolving regeneration of living tissues. Critically, 4D Bioassembly transcends the concept of ‘3D + time’ by introducing the capacity for active, dynamic adaptation post-implantation. The construct is not merely a pre-formed static scaffold that degrades over time, but an intelligent system designed to sense, interpret, and respond to evolving host microenvironmental cues through built-in feedback loops, thereby guiding the regenerative process with spatiotemporal precision. This ambition finds its fundamental rationale in developmental biology. The native osteochondral unit, particularly the growth plate, serves as the quintessential blueprint for such spatiotemporal organization. As elucidated by Lui et al. [ 40 ], the mammalian growth plate operates through a precisely coordinated spatial and temporal regulation of gene expression, governing zonal differentiation and cellular maturation over time. This intrinsic biological program demonstrates that the functional regeneration of osteochondral tissues is inherently a 4D process. Our proposed paradigm, therefore, does not emerge from a vacuum but seeks to replicate this sophisticated native control through engineering means. The operational mechanism of this paradigm is illustrated in (Fig. 10 ). As shown, the 4D Bioassembly scaffold achieves functional regeneration by dynamically responding to key microenvironmental signals (e.g., low pH, aggrecanase-1). This responsiveness triggers the programmed, spatiotemporal release of bioactive factors (such as VEGF, TGF-β, and BMP-2) to orchestrate angiogenesis, chondrogenesis, and osteogenesis. This feedback-driven interaction is the cornerstone of the 4D Bioassembly concept, enabling a truly adaptive and biomimetic regeneration process. Critically, the concept of 4D Bioassembly is further substantiated by strategies that recapitulate developmental processes. For instance, Mendes et al. [ 41 ] engineered a living, bilayered implant that mimicked an immature osteochondral graft. Upon implantation, this construct not only integrated with the host tissue but also progressively matured over time, re-establishing a structured osteochondral unit with a distinct tidemark and subchondral bone plate. This exemplifies the core of 4D Bioassembly: the creation of a living implant that is spatiotemporally programmed to self-evolve into a mature, functional tissue after implantation. This evidence bridges our bibliometric findings with concrete experimental validation, confirming that the field is indeed transitioning from conceptual aspiration to tangible implementation. In summary, this study decodes osteochondral repair as a discipline propelled by a translational feedback loop, charted through a triple-paradigm evolution, and currently challenged by global collaboration asymmetry. The field’s trajectory is unequivocally defined by its progression into the Functional Mimicry paradigm and is now pivoting toward the ultimate frontier of 4D Bioassembly. Future breakthroughs, therefore, hinge on strategically bridging collaboration network disparities and fostering deeper interdisciplinary integration to achieve the spatiotemporally programmed regeneration of fully functional osteochondral tissues. The 4D Bioassembly scaffold achieves functional regeneration by dynamically responding to key microenvironmental signals (e.g., low pH, aggrecanase-1) to trigger the programmed release of bioactive factors (VEGF, TGF-β, BMP-2) for angiogenesis, chondrogenesis, and osteogenesis. Limitations and Future Prospects This study has several inherent limitations that should be considered. The exclusive reliance on the Web of Science Core Collection for literature sourcing may introduce a selection bias, as relevant publications in other databases or in non-English journals were not included. Furthermore, as a macro-trend analytical tool, bibliometrics excels at mapping the knowledge landscape but cannot assess the methodological rigor or the risk of bias within individual primary studies—a critical task that remains the domain of systematic reviews. Guided by the findings of this analysis, we propose three strategic directions for future endeavors to accelerate the field’s advancement: Deepening Interdisciplinary Integration : Future progress hinges on forming deeply integrated teams that combine expertise from materials science, developmental biology, and clinical medicine. Such collaboration is essential to tackle the core challenges of “functionalized regeneration,” including achieving robust neurovascular integration and ensuring long-term biomechanical compatibility, thereby strengthening the translational feedback loop. Optimizing Global Collaboration Networks : There is a pressing need to encourage strategic partnerships between high-output research institutions in emerging countries and the core hubs within the global innovation network. This initiative is critical to address the identified global research asymmetry, helping to bridge the “centrality gap” and promote a more balanced and efficient flow of knowledge and talent. Bridging the Translational Pathway for 4D Bioassembly : To realize the potential of the 4D Bioassembly paradigm, the field must focus on standardizing biofabrication protocols, conducting rigorously controlled large-scale clinical trials, and implementing comprehensive long-term outcome assessments to evaluate the safety, efficacy, and durability of the regenerated osteochondral tissues. 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Bone 46:1380–1390 Mendes LF, Bosmans K, Van Hoven I, Viseu SR, Marechal M, Luyten FP (2020) Developmental engineering of living implants for deep osteochondral joint surface defects. Bone 139:115520 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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06:51:56","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130801,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/7c19cef4d903afe38d5c9e9a.html"},{"id":101207335,"identity":"7d673622-f17e-45a7-a1fe-7b6b0a34319d","added_by":"auto","created_at":"2026-01-27 10:02:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":511208,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual publication output and citation impact trends (2000–2024).\u003cbr\u003e\nA Publication volume. The annual number of publications shows a fluctuating upward trend, with a peak in 2021. B Citation metrics. The blue line depicts the annual number of publications. The red line shows the cumulative number of citations received.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/868170a8057d29098fed1827.png"},{"id":101189266,"identity":"6b3eafe1-a6d0-49f8-8042-710375cac5e2","added_by":"auto","created_at":"2026-01-27 06:51:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1172480,"visible":true,"origin":"","legend":"\u003cp\u003eDisciplinary distribution of osteochondral repair research.\u003cbr\u003e\nFrequency of research area assignments (Total assignments: 10,419 from 2,919 publications). Red curve: Field Proportion (%). Crossover index = 357.01% (Total assignments/Total publications).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/00494ff4789140ac87ca9d2f.png"},{"id":101189264,"identity":"a7d62668-eef4-47d4-81db-05e740115930","added_by":"auto","created_at":"2026-01-27 06:51:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":558842,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal collaboration networks.\u003cbr\u003e\nA Country-level network. Network of international co-authorships (2000-2024). Node size: publication count; line thickness: collaboration strength; node color (red-higher): betweenness centrality. B Institutional network (2000–2024). Network visualization of inter-institutional co-authorship patterns. Node size corresponds to publication output; line thickness indicates collaboration strength; node color reflects betweenness centrality (warmer hues indicate higher values). The network structure reveals distinct collaborative clusters with the University of California System occupying the most central position.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/b9673f498c35c116939ce5f8.png"},{"id":101189267,"identity":"45fb2e00-85b3-4378-9999-ed5a16cd848b","added_by":"auto","created_at":"2026-01-27 06:51:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1267328,"visible":true,"origin":"","legend":"\u003cp\u003eAuthor collaboration network in osteochondral repair studies.\u003cbr\u003e\nCo-authorship network of 49 authors. Node size corresponds to publication count; line thickness represents collaboration strength. Nodes are colored based on cluster analysis.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/f3c8411e1dddda156d8e4332.png"},{"id":101207008,"identity":"bc0254dc-061c-451f-8753-1019a67cdcc2","added_by":"auto","created_at":"2026-01-27 09:57:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1057717,"visible":true,"origin":"","legend":"\u003cp\u003eCo-citation network analysis.\u003cbr\u003e\nA Document co-citation. Network visualization of co-cited references generated using VOSviewer. Node size corresponds to citation frequency. Colors indicate distinct thematic clusters: red (Cluster 1), green (Cluster 2), and blue (Cluster 3). Lines represent co-citation relationships between references. B Journal co-citation. This figure was generated through journal co-citation analysis. Node size represents the frequency of journal co-citation, while line thickness indicates the strength of co-citation relationships. Node colors, automatically generated based on cluster analysis, identify three major research clusters.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/32ad65b4ba759364b36b90e6.png"},{"id":101206899,"identity":"abcd249e-0b67-4f52-aa06-f074cea6dad0","added_by":"auto","created_at":"2026-01-27 09:56:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":336537,"visible":true,"origin":"","legend":"\u003cp\u003eInterdisciplinary knowledge flow trajectories revealed by dual-map overlay.\u003cbr\u003e\nThis dual-map overlay illustrates interdisciplinary knowledge exchange through citation trajectories. The width of each curve corresponds to the z-score (measuring the statistical strength of the connection), while the frequency (f) represents the raw count of citation instances. Source disciplines are shown on the left, and target research fronts are displayed on the right.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/6733cd865497fcf6a3af3282.png"},{"id":101189286,"identity":"d7a1de29-8de2-4bd0-8189-1deedefd936b","added_by":"auto","created_at":"2026-01-27 06:51:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":975349,"visible":true,"origin":"","legend":"\u003cp\u003eKeyword co-occurrence and thematic clustering analysis.\u003cbr\u003e\nA Co-occurrence network. Network generated using VOSviewer. Node size corresponds to keyword frequency; link thickness indicates co-occurrence strength. Colored clusters represent distinct research themes in the field. B Clustering map (2000–2024). The network was pruned using the Pathfinder algorithm. Modularity (Q = 0.4377) and weighted mean silhouette (S = 0.7019) indicate a significant and credible cluster structure. Five major clusters were identified and labeled using the log-likelihood ratio (LLR) algorithm.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/b05687bf308b7b411223acfd.png"},{"id":101189283,"identity":"33ebb4f0-dc55-4341-a943-e88bf96f3e84","added_by":"auto","created_at":"2026-01-27 06:51:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":687688,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal evolution of research hotspots.\u003cbr\u003e\nA Top 20 keywords with strongest citation bursts. Keywords are ranked by burst strength, which indicates a sharp increase in citation frequency within a specific period. The red line segment represents the duration of the burst. B Keyword timezone view. The visualization maps the emergence and evolution of research themes based on keyword co-occurrence. The horizontal axis represents publication years. Node size reflects keyword frequency, and links represent co-occurrence relationships between keywords.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/52a1507f445f591c2bd8feb4.png"},{"id":101189278,"identity":"5376e9b7-132a-40aa-9aaf-079450d51ed0","added_by":"auto","created_at":"2026-01-27 06:51:55","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1441534,"visible":true,"origin":"","legend":"\u003cp\u003eEvolutionary pathway of osteochondral repair paradigms: From Structural Repair to 4D Bioassembly.\u003cbr\u003e\nThe conceptual evolution of osteochondral repair has progressed through three sequential paradigms—Structural Repair, Biological Regeneration, and Functional Mimicry—and is now advancing toward the emerging frontier of 4D Bioassembly, which aims for spatiotemporally programmed, self-evolving regeneration.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/3124a0f5a8a00e75f4b43223.png"},{"id":101189276,"identity":"6f3f5d32-4e6d-481b-b54f-6f0ca7b3b849","added_by":"auto","created_at":"2026-01-27 06:51:55","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2949072,"visible":true,"origin":"","legend":"\u003cp\u003eMechanism of 4D Bioassembly: Microenvironment-responsive spatiotemporal programming for functional regeneration.\u003cbr\u003e\nThe 4D Bioassembly scaffold achieves functional regeneration by dynamically responding to key microenvironmental signals (e.g., low pH, aggrecanase-1) to trigger the programmed release of bioactive factors (VEGF, TGF-β, BMP-2) for angiogenesis, chondrogenesis, and osteogenesis.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/03cfd428d656eb4a822ad7a4.png"},{"id":105567891,"identity":"4cbc1ff1-5d3f-4923-86b0-703ce98e8c71","added_by":"auto","created_at":"2026-03-27 13:05:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11550758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8681412/v1/a9ff4280-b5e3-4920-a1dc-76d57781ec49.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003e4D Bioassembly: Evolutionary Pathway and Future of Osteochondral Repair via Bibliometric Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArticular cartilage injury is a prevalent orthopedic condition, arising from causes such as overuse, trauma, and degeneration [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], yet its repair is severely constrained by the tissue\u0026rsquo;s avascular, alymphatic, and aneural nature [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The progressive involvement of the subchondral bone leads to osteochondral defect formation [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], subsequently aggravating joint dysfunction and pain while substantially compromising patient quality of life [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Currently, standard clinical interventions for articular cartilage repair primarily comprise microfracture (MF), autologous or allogeneic osteochondral transplantation (OCT), and autologous chondrocyte implantation (ACI) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, these approaches present significant limitations. For instance, MF often results in the formation of fibrocartilage, which exhibits inferior biomechanical properties compared with native hyaline cartilage [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Other limitations include poor integration with the host tissue and long-term degeneration post-transplantation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], as well as the uncertain long-term efficacy of ACI technology [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsequently, there is a pressing clinical need to develop novel therapeutic strategies capable of achieving both biological and functional restoration. Since the 1990s, osteochondral repair strategies have undergone significant evolution. The initial stage focused on marrow stimulation techniques, such as MF, which aimed to promote defect filling by triggering a repair response or implanting active cells [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequently, osteochondral transplantation techniques were developed; however, their application has been constrained by limitations in donor availability, risks of immune rejection, and potential disease transmission [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The recent emergence of tissue engineering has provided novel directions for osteochondral regeneration. By integrating three-dimensional porous scaffolds, bioactive factors, and stem cell technology, tissue engineering strategies aim to biomimetically construct osteochondral composite tissues with gradient structures and biological functions, thereby achieving more effective and durable repair outcomes [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe rapid advancement in the field of osteochondral repair has been accompanied by an exponential growth in related scientific publications, encompassing diverse aspects such as material design, fabrication techniques, in vitro and in vivo evaluation, and clinical translation. Confronted with this vast and fragmented knowledge landscape, conventional review methodologies struggle to provide a comprehensive and objective perspective on the field\u0026rsquo;s overall developmental trajectory, collaborative network structures, and thematic evolution. Bibliometrics, as a quantitative analysis method for scientific literature, offers the capability to reveal latent research trends, identify core authors and institutional collaboration patterns, and detect emerging frontiers within the discipline [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Through a systematic analysis of relevant literature published from 2000 to 2024, this study aims to construct a knowledge map of the osteochondral repair field.\u003c/p\u003e \u003cp\u003eBuilding upon this foundation, we employ a multi-method bibliometric analysis not merely to describe, but to decode the field of osteochondral repair. We seek to transcend traditional description by pursuing four pivotal intellectual contributions: First, to move beyond listing hotspots to delineate the fundamental paradigms that have defined the field\u0026rsquo;s evolution. Second, to uncover the intrinsic mechanisms of knowledge translation, testing whether the field operates as a linear pipeline or a more dynamic, interactive system. Third, to diagnose the global collaborative anatomy, assessing not just who produces knowledge, but who truly connects and leads the global network. Finally, by synthesizing these insights, we aim to forecast strategic frontiers and define the next research paradigm. Through this approach, we seek to provide not just a map of the past, but a compass for the future, offering a data-driven roadmap for the next decade of innovation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources and Search Strategy\u003c/h2\u003e \u003cp\u003eOn July 30, 2025, a systematic retrieval was conducted utilizing the Web of Science Core Collection (WoSCC) database to identify literature on osteochondral repair published between January 1, 2000, and December 31, 2024. The search strategy was constructed based on the PICO framework, combining core concepts including \u0026ldquo;osteochondral defect/injur,\u0026rdquo; \u0026ldquo;repair/regenerat/tissue engineer,\u0026rdquo; and \u0026ldquo;hydrogel/scaffold/biomaterial\u0026rdquo; in the Topic (TS) and Title (TI) fields. The detailed search strategy is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSearch strategy in Web of Science Core Collection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScope\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS=(\u0026ldquo;osteochondral defect\u0026rdquo; OR \u0026ldquo;osteochondral injury\u0026rdquo; OR \u0026ldquo;osteochondral lesion\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTS=(\u0026ldquo;microfracture\u0026rdquo; OR \u0026ldquo;autologous chondrocyte implantation\u0026rdquo; OR \u0026ldquo;ACI\u0026rdquo; OR \u0026ldquo;osteochondral autograft transfer\u0026rdquo; OR \u0026ldquo;OATS\u0026rdquo; OR \u0026ldquo;matrix-induced autologous chondrocyte implantation\u0026rdquo; OR \u0026ldquo;MACI\u0026rdquo; OR \u0026ldquo;osteochondral allograft\u0026rdquo; OR regenerat OR scaffold* OR \u0026ldquo;tissue engineering\u0026rdquo; OR hydrogel OR \u0026ldquo;3D print*\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#1 AND #2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOT WC=(\u0026ldquo;Veterinary Sciences\u0026rdquo; OR \u0026ldquo;Dentistry Oral Surgery Medicine\u0026rdquo; OR \u0026ldquo;Oncology\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#3 NOT #4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#5 AND PY=(2000\u0026ndash;2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#6 AND DT=(Article OR Review)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoc Type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#7 AND LA=(English)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinal result: #8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*TS: Topic field; WC: Web of Science category; PY: Publication year; DT: Document type; LA: Language.\u003c/p\u003e \u003cp\u003e*Note: The literature time scope of this study is set to 2000\u0026ndash;2024, and the final retrieval was conducted on July 30, 2025. This is to avoid the 1\u0026ndash;3 month indexing lag of databases after journal publication, ensuring the complete inclusion of literatures published in 2024 and reducing the risk of missed retrieval.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLiterature Screening Process\u003c/h3\u003e\n\u003cp\u003eThe literature screening was performed independently by two researchers according to the PRISMA guidelines. Titles and abstracts of the initially identified 2,934 records were screened against predefined eligibility criteria.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusion Criteria\u003c/b\u003e were:\u003c/p\u003e \u003cp\u003e(1) studies focusing on the repair or regeneration of osteochondral or articular cartilage defects;\u003c/p\u003e \u003cp\u003e(2) investigations involving tissue engineering or regenerative medicine strategies (e.g., scaffolds, hydrogels, 3D printing, cell therapy);\u003c/p\u003e \u003cp\u003e(3) original research articles (in vitro, in vivo, or clinical) or systematic reviews;\u003c/p\u003e \u003cp\u003e(4) publications dated between 2000 and 2024; and\u003c/p\u003e \u003cp\u003e(5) English-language publications.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion Criteria\u003c/b\u003e included:\u003c/p\u003e \u003cp\u003e(1) studies irrelevant to osteochondral repair;\u003c/p\u003e \u003cp\u003e(2) research on non-articular cartilage or non-orthopedic fields;\u003c/p\u003e \u003cp\u003e(3) studies focusing on periprosthetic osteolysis, infectious arthritis, or conservative osteoarthritis management;\u003c/p\u003e \u003cp\u003e(4) non-peer-reviewed publications; and\u003c/p\u003e \u003cp\u003e(5) articles with unavailable full text.\u003c/p\u003e \u003cp\u003eThrough this process, 15 records were excluded (3 retracted articles and 12 irrelevant publications), resulting in 2,919 publications for final analysis.\u003c/p\u003e\n\u003ch3\u003eAnalytical Methods and Tools\u003c/h3\u003e\n\u003cp\u003eBibliographic records exported from WoSCC constituted the primary dataset. A multi-method analytical framework was employed:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDescriptive Analysis\u003c/b\u003e: Fundamental characteristics (e.g., annual output, journal distribution) were quantified using Microsoft Excel.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eScience Mapping\u003c/b\u003e: VOSviewer was used to construct and visualize co-occurrence and co-citation networks, with cluster analysis identifying research themes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEvolutionary and Frontier Analysis\u003c/b\u003e: CiteSpace was employed for keyword burst detection, timeline visualization, dual-map overlay analysis, and collaboration network analysis to uncover trends and frontiers.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis integrated framework was designed to elucidate the knowledge structure and developmental trajectory of osteochondral repair research from macro- to micro-level perspectives.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnnual Publication Trends, Citation Impact, and Disciplinary Distribution\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eAnalysis of Literature Collection and Annual Trends\u003c/h2\u003e \u003cp\u003eThis study was based on the Web of Science Core Collection database (SCI-EXPANDED), through which a systematic retrieval of research literature on osteochondral repair published between 2000 and 2024 was conducted. Following a rigorous screening process, a total of 2,919 academic publications meeting the predefined criteria were ultimately included, establishing the baseline dataset for this bibliometric analysis. Time-series analysis of this dataset revealed distinct developmental dynamics within the field (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). During the initial stage (2000\u0026ndash;2004), annual output remained low (average: 29.4 publications/year), reflecting a phase of technological exploration. The field then entered a period of rapid growth (2005\u0026ndash;2013), with annual output surging to an average of 95.4 publications and exceeding 100 for the first time in 2013. From 2014 to 2020, research entered a stable developmental plateau, maintaining a high annual output (average: 159.1 publications), which confirms its establishment as a mature subfield. Publication output peaked in 2021 (220 publications). The sustained high output in the most recent years (2022\u0026ndash;2023) robustly confirms the field\u0026rsquo;s continued vitality and innovative potential.\u003c/p\u003e \u003cp\u003eAnalysis of Citation Impact\u003c/p\u003e \u003cp\u003eCitation analysis underscores the substantial impact of osteochondral repair research (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The 2,919 publications received 127,588 total citations, with 101,804 non-self-citations, demonstrating broad academic resonance. The field exhibits a high average of 43.71 citations per article and an H-index of 152, collectively affirming the high impact and quality of its research output.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA Publication volume. The annual number of publications shows a fluctuating upward trend, with a peak in 2021. B Citation metrics. The blue line depicts the annual number of publications. The red line shows the cumulative number of citations received.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eDisciplinary Distribution and Interdisciplinary Characteristics\u003c/h3\u003e\n\u003cp\u003eAnalysis of disciplinary co-occurrence confirms the interdisciplinary nature of osteochondral repair research, with publications spanning over 80 Web of Science categories. Orthopedics formed the dominant core (1,349 publications, 46.21%), establishing the clinical foundation. A robust core research cluster included Biomedical Engineering (22.71%), Sport Sciences (19.80%), Materials Science Biomaterials (19.53%), and Surgery (17.85%), driving advancements in materials, biomechanics, and clinical translation. The significant presence of Cell \u0026amp; Tissue Engineering (10.93%) and Cell Biology (8.36%) highlights regenerative medicine as a key frontier. The sum of percentages exceeding 100% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) directly evidences the frequent cross-disciplinary classification of publications in this field.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrequency of research area assignments (Total assignments: 10,419 from 2,919 publications). Red curve: Field Proportion (%). Crossover index\u0026thinsp;=\u0026thinsp;357.01% (Total assignments/Total publications).\u003c/p\u003e\n\u003ch3\u003eAnalysis of National and Institutional Collaboration Networks\u003c/h3\u003e\n\u003cp\u003eCollaboration networks among countries and institutions were mapped using CiteSpace to assess the global research landscape (2000\u0026ndash;2023).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of National Collaboration Networks\u003c/h2\u003e \u003cp\u003eThe United States led in both publication output (819) and betweenness centrality (0.55), positioning it as the central hub of the global network (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). China ranked second in output (548) but had low centrality (0.11). Several European countries, including Germany, Italy, England, and the Netherlands, also showed significant centrality, acting as important connectors. Visually, the USA and key European nations form the network core with dense connections, whereas China, despite its high output, is peripherally positioned with fewer and weaker links.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 productive countries and their centrality metrics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBegin Year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Note: Betweenness centrality measures a country\u0026rsquo;s role as a connector in the global collaboration network. Higher values indicate greater importance as a bridge.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Institutional Collaboration Networks\u003c/h2\u003e \u003cp\u003eIRCCS Istituto Ortopedico Rizzoli (88 publications) and the University of London (81) were the most productive institutions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The University of California system held the highest centrality (0.12), indicating its pivotal bridging role. While several Chinese institutions, namely Shanghai Jiao Tong University (5th), Chinese Academy of Sciences (8th), Peking University (9th), and Zhejiang University (10th), ranked among the top 10 in productivity (as shown in Table\u0026nbsp;3), all exhibited low centrality (below 0.05). The network core is composed of the University of California system, Harvard University and its affiliates, and leading European institutions, with Chinese institutions predominantly in non-core positions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA Country-level network. Network of international co-authorships (2000\u0026ndash;2024). Node size: publication count; line thickness: collaboration strength; node color (red-higher): betweenness centrality. B Institutional network (2000\u0026ndash;2024). Network visualization of inter-institutional co-authorship patterns. Node size corresponds to publication output; line thickness indicates collaboration strength; node color reflects betweenness centrality (warmer hues indicate higher values). The network structure reveals distinct collaborative clusters with the University of California System occupying the most central position.\u003c/p\u003e \u003cp\u003eThe collaboration network metrics reveal a distinct core-periphery architecture, with the United States as the central hub. The marked discrepancy between China\u0026rsquo;s publication output and its betweenness centrality delineates a quantifiable asymmetry in global collaborative influence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Author Collaboration Networks\u003c/h2\u003e \u003cp\u003eThe author collaboration network, constructed using VOSviewer, comprised 49 authors forming 7 major clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Italian researchers Filardo, Giuseppe and Kon, Elizaveta occupied the most central positions as international collaboration hubs, whereas the team led by American scholar Kennedy, John G. had the highest publication count (42) but a more concentrated collaborative scope. Network analysis identified four primary clusters: an Italian cluster focused on clinical translation; a North American cluster specializing in sports medicine; a multinational European cluster concentrating on basic research and tissue engineering; and a Japanese cluster emphasizing biomaterials and implant development. This regionally and thematically specialized architecture substantiates the field\u0026rsquo;s tripartite intellectual structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCo-authorship network of 49 authors. Node size corresponds to publication count; line thickness represents collaboration strength. Nodes are colored based on cluster analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIntellectual Base: Document Co-citation and Journal Co-citation Analysis\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eAnalysis of Document Co-citation Networks\u003c/h2\u003e \u003cp\u003eThe intellectual structure of the osteochondral repair research field is organized into three major clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA): Cluster 1 (Red): This cluster is centered around foundational publications such as Knutsen G (2004), with its constituent literature primarily focusing on randomized controlled trials and long-term efficacy evaluation; Cluster 2 (Green): This cluster is anchored by seminal works including Brittberg M (1994), encompassing literature that predominantly addresses chondral biological mechanisms, cell-based therapies, and tissue regeneration; Cluster 3 (Blue): This cluster features pivotal studies such as Chuckpaiwong B (2008), with its associated publications mainly concentrating on surgical technique applications, clinical outcomes, and complication analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Journal Co-citation Network\u003c/h2\u003e \u003cp\u003eThe intellectual structure of the field was further deconstructed through journal co-citation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The cluster anchored by \u003cem\u003eThe American Journal of Sports Medicine\u003c/em\u003e (green nodes) corresponds to the clinical orthopedics and sports medicine domain, emphasizing surgical techniques, treatment outcome evaluation, and optimization of clinical efficacy; the cluster featuring \u003cem\u003eJournal of Orthopaedic Research\u003c/em\u003e (blue nodes) reflects the basic science and bone biology orientation, dedicated to fundamental research such as mechanisms of cartilage degeneration, cell differentiation, and regenerative processes. The strong co-citation relationships among these clusters highlight the highly interdisciplinary nature of the field, whose advancement relies on the deep integration and synergistic interaction among material technology innovation, clinically driven problem-solving, and fundamental scientific discovery.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA Document co-citation. Network visualization of co-cited references generated using VOSviewer. Node size corresponds to citation frequency. Colors indicate distinct thematic clusters: red (Cluster 1), green (Cluster 2), and blue (Cluster 3). Lines represent co-citation relationships between references. B Journal co-citation. This figure was generated through journal co-citation analysis. Node size represents the frequency of journal co-citation, while line thickness indicates the strength of co-citation relationships. Node colors, automatically generated based on cluster analysis, identify three major research clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInterdisciplinary Mapping: Dual-map Overlay\u003c/h2\u003e \u003cp\u003eThe dual-map overlay clearly delineates three primary knowledge-source disciplines and their cross-domain flow trajectories within osteochondral repair research (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The purple trajectory demonstrates substantial knowledge transfer from Physics/Materials/Chemistry (left #5) to applied Chemistry/Materials clusters (right #4; z\u0026thinsp;=\u0026thinsp;2.74, f\u0026thinsp;=\u0026thinsp;1641) and Molecular Biology/Genetics clusters (right #8; z\u0026thinsp;=\u0026thinsp;2.38, f\u0026thinsp;=\u0026thinsp;1456), indicating material science\u0026rsquo;s dual role in driving both biomaterial development and basic biological research. Simultaneously, the yellow trajectory reveals Molecular Biology/Immunology (left #4) as an interdisciplinary hub, disseminating knowledge to Chemistry/Materials (right #4; z\u0026thinsp;=\u0026thinsp;1.70, f\u0026thinsp;=\u0026thinsp;1108), Molecular Genetics (right #8; z\u0026thinsp;=\u0026thinsp;3.95, f\u0026thinsp;=\u0026thinsp;2256), and Sports/Rehabilitation clusters (right #9; z\u0026thinsp;=\u0026thinsp;2.04, f\u0026thinsp;=\u0026thinsp;1284), thereby bridging basic science and clinical applications. Most significantly, the gray trajectory exhibits a robust reverse knowledge flow from clinical disciplines\u0026mdash;Neurology/Sports/Ophthalmology (left #8)\u0026mdash;to all three major research frontiers (right #4: z\u0026thinsp;=\u0026thinsp;2.06, f\u0026thinsp;=\u0026thinsp;1291; right #8: z\u0026thinsp;=\u0026thinsp;3.75, f\u0026thinsp;=\u0026thinsp;2152; right #9: z\u0026thinsp;=\u0026thinsp;3.32, f\u0026thinsp;=\u0026thinsp;1984), highlighting a critical feedback mechanism wherein clinical practice actively informs and directs fundamental scientific exploration, ultimately reflecting the deeply integrated and bidirectional nature of knowledge exchange in this field.\u003c/p\u003e \u003cp\u003eThe combined evidence from co-citation and dual-map analyses reveals a systematically integrated knowledge architecture in osteochondral repair, defined by a tripartite intellectual structure connected through a bidirectional knowledge flow and, most crucially, a pervasive translational feedback loop. This loop is evidenced by a strong reverse citation trajectory from clinical disciplines to basic science, whereby clinical insights directly catalyze foundational and technological inquiry, which in turn cycles back to advance clinical practice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis dual-map overlay illustrates interdisciplinary knowledge exchange through citation trajectories. The width of each curve corresponds to the z-score (measuring the statistical strength of the connection), while the frequency (f) represents the raw count of citation instances. Source disciplines are shown on the left, and target research fronts are displayed on the right.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eResearch Focus and Evolution: Analysis of Keyword Co-occurrence, Clustering, and Burst Detection\u003c/h2\u003e \u003cp\u003eTo systematically elucidate the research hotspots, knowledge structure, and evolutionary trajectory in the field of osteochondral repair, this study employed both VOSviewer and CiteSpace for keyword co-occurrence and burst detection analyses. The results from these methods were integrated to provide a comprehensive understanding of the field\u0026rsquo;s thematic architecture and its dynamic shifts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThematic Architecture from Co-occurrence Clustering\u003c/h2\u003e \u003cp\u003eKeyword co-occurrence analysis revealed the field\u0026rsquo;s thematic structure through two complementary visualizations. VOSviewer identified distinct color-coded clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), while CiteSpace delineated five major clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), both reflecting core research themes:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBasic Science \u0026amp; Tissue Engineering (VOSviewer Red / CiteSpace #1)\u003c/b\u003e: Focused on mechanisms and strategies, with core keywords including mesenchymal stem cells, tissue engineering, and chondrogenic differentiation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBiomaterial Design \u0026amp; Regeneration (CiteSpace #2)\u003c/b\u003e: Characterized by a thematic focus on the fabrication of biomaterials for regenerative applications, with keywords such as bilayered scaffold, biphasic scaffold, and 3D printing. These keywords reflect a central research drive: engineering scaffolds with increasingly sophisticated and biomimetic structural complexity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClinical Interventions (VOSviewer Green / CiteSpace #0)\u003c/b\u003e: Predominantly concerned with clinical techniques, encompassing keywords like transplantation, microfracture, and autologous chondrocyte implantation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBioactive Factors (VOSviewer Blue)\u003c/b\u003e: Highlighting the therapeutic applications of bioactive factors, with terms including platelet-rich plasma, knee osteoarthritis, and hyaluronic acid.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePathology \u0026amp; Outcomes (CiteSpace #3, #4)\u003c/b\u003e: Complementing the structure by focusing on \u003cem\u003eosteochondral defect/injury\u003c/em\u003e models and \u003cem\u003eclinical outcome\u003c/em\u003e evaluation, respectively.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA Co-occurrence network. Network generated using VOSviewer. Node size corresponds to keyword frequency; link thickness indicates co-occurrence strength. Colored clusters represent distinct research themes in the field. B Clustering map (2000\u0026ndash;2024). The network was pruned using the Pathfinder algorithm. Modularity (Q\u0026thinsp;=\u0026thinsp;0.4377) and weighted mean silhouette (S\u0026thinsp;=\u0026thinsp;0.7019) indicate a significant and credible cluster structure. Five major clusters were identified and labeled using the log-likelihood ratio (LLR) algorithm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTemporal Evolution from Burst Detection and Timezone View\u003c/h2\u003e \u003cp\u003eKeyword burst detection analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) identifies research topics with sharp increases in citation frequency, while the timezone view (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) visually displays their chronological emergence. Combined, these methods reveal a distinct temporal evolution:\u003c/p\u003e \u003cp\u003e \u003cb\u003e2000\u0026ndash;2010 (Structural Repair)\u003c/b\u003e: This phase was defined by keywords with the highest burst strength, including full-thickness defects (strength: 24.60), osteochondral defects (strength: 25.12), and autologous chondrocyte transplantation (strength: 7.40), indicating a primary focus on defect management and first-generation surgical techniques.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2011\u0026ndash;2017 (Biological Regeneration)\u003c/b\u003e: The research focus shifted, marked by the emergence of keywords such as randomized trial (strength: 7.81) and the rise of scaffold, mesenchymal stem cells, and platelet-rich plasma. This signifies a transition towards evidence-based medicine and active regeneration using cells and bioactive factors.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2018\u0026ndash;Present (Functional Mimicry)\u003c/b\u003e: The current frontier is authoritatively defined by the most powerful burst keyword, osteochondral regeneration (strength: 19.93), signaling a definitive shift in the field\u0026rsquo;s ambition from repair to true regeneration. This goal is operationally driven by the sustained citation bursts of key enabling technologies. The data reveal a powerful convergence: 3D printing (strength: 7.90) provides the capability for architectural precision, while hydrogel (strength: 8.18) introduces essential dynamic functionality. The co-emergence of these technologies signifies that the field\u0026rsquo;s focus has expanded from creating static structural mimics to engineering constructs capable of supporting more complex biological processes. The powerful convergence of the regenerative objective with the means of advanced fabrication and smart materials provides compelling empirical evidence that the field is evolving beyond the current Functional Mimicry paradigm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA Top 20 keywords with strongest citation bursts. Keywords are ranked by burst strength, which indicates a sharp increase in citation frequency within a specific period. The red line segment represents the duration of the burst. B Keyword timezone view. The visualization maps the emergence and evolution of research themes based on keyword co-occurrence. The horizontal axis represents publication years. Node size reflects keyword frequency, and links represent co-occurrence relationships between keywords.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis convergence is not an endpoint; rather, it represents the active assembly of a technological foundation that is inherently dynamic and adaptive. We interpret this foundational shift as the empirical emergence of a new paradigm, which we conceptualize as 4D Bioassembly. This multidimensional bibliometric analysis delineates the maturation of osteochondral repair through three core mechanisms that explain this progression: the function of a \u0026ldquo;translational feedback loop,\u0026rdquo; the structure of \u0026ldquo;global research asymmetry,\u0026rdquo; and the trajectory of a \u0026ldquo;triple-paradigm evolution.\u0026rdquo; Together, these mechanisms not only decode the field\u0026rsquo;s past development but also provide a strategic framework for navigating its future trajectory toward 4D Bioassembly.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePivoting from Functional Mimicry toward 4D Bioassembly\u003c/h2\u003e \u003cp\u003eThe temporal and thematic progression of keywords, when integrated, provides robust evidence for the field\u0026rsquo;s triple-paradigm evolution. The centrality of microfracture and transplantation in the early-stage clusters (e.g., Cluster #0, Green VOSviewer cluster) anchors the Structural Repair paradigm, as evidenced by influential clinical research of the era [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The subsequent rise of tissue engineering, mesenchymal stem cells, and scaffolds signals the shift to the Biological Regeneration paradigm. Finally, the emergence and dominance of osteochondral regeneration as a core cluster (#2), specifically defined by 3D printing and fabrication and confirmed by its high burst strength, clearly demarcates the current frontier of Functional Mimicry, focused on replicating the native tissue\u0026rsquo;s complex architecture. This established evolutionary pathway not only explains the past but also clearly signals the necessity to pivot toward the emerging paradigm of 4D Bioassembly (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe conceptual evolution of osteochondral repair has progressed through three sequential paradigms\u0026mdash;Structural Repair, Biological Regeneration, and Functional Mimicry\u0026mdash;and is now advancing toward the emerging frontier of 4D Bioassembly, which aims for spatiotemporally programmed, self-evolving regeneration.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eEvolution of the Intellectual Structure and Its Interdisciplinary Nature\u003c/h2\u003e \u003cp\u003eThe field\u0026rsquo;s tripartite intellectual structure\u0026mdash;integrating biomaterials and tissue engineering, clinical orthopedics and sports medicine, and basic bone and cartilage biology\u0026mdash;functions not in isolation but as a synergistically evolving organic whole, forming a translational feedback loop.\u003c/p\u003e \u003cp\u003eThis integrative dynamic is evident throughout the field\u0026rsquo;s history. For instance, the clinical refinement of ACI, from demonstrating long-term efficacy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] to addressing complex degenerative conditions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], has been profoundly dependent on foundational biological discoveries. Seminal work on the mechanisms of cartilage repair [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and cellular differentiation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] provided the theoretical basis for such regenerative strategies, while advances in biomaterial design [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] offered the means to execute them.\u003c/p\u003e \u003cp\u003eConversely, clinical investigations actively guide fundamental research. Studies evaluating the limitations of microfracture technique highlighted unmet clinical needs, motivating the development of enhanced technologies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, research elucidating the mechanism of action of platelet-rich plasma (PRP) established its biological rationale and paved the way for its broad application [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis evidence demonstrates that the field operates not as a linear pipeline but as a dynamic, integrated system\u0026mdash;a translational feedback loop. This fundamental mechanism ensures that clinical challenges seed scientific inquiry, and technological breakthroughs are rapidly validated against real-world needs, creating a virtuous cycle that drives the field\u0026rsquo;s advancement.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGlobal Research Asymmetry and Strategic Collaboration\u003c/h2\u003e \u003cp\u003eBeyond its integrated intellectual core, the field\u0026rsquo;s growth is shaped by a distinct global collaboration anatomy. Our analysis reveals a pronounced asymmetry between research output and collaborative influence on a global scale.\u003c/p\u003e \u003cp\u003eThe United States functions as the central hub, a position sustained by its robust research ecosystem, clinical resources, and ability to attract elite talent, as reflected in the high centrality of institutions like the University of California system and Harvard University. European nations form strong secondary clusters through dense intraregional collaboration, playing a critical \u0026ldquo;bridging\u0026rdquo; role in global knowledge flow.\u003c/p\u003e \u003cp\u003eIn contrast, several high-output countries exhibit relatively low betweenness centrality, indicating that their institutions act more as \u0026ldquo;major contributors\u0026rdquo; than \u0026ldquo;global hubs.\u0026rdquo; This disparity may be influenced by historical development trajectories, international collaboration patterns, and linguistic or cultural factors.\u003c/p\u003e \u003cp\u003eTherefore, this global research asymmetry represents a critical strategic bottleneck. For high-output yet peripheral countries, future progress hinges on fostering strategic partnerships with core international teams and shifting the emphasis from quantitative output to enhancing pivotal roles within the global innovation network. Bridging this \u0026ldquo;centrality gap\u0026rdquo; is imperative for unlocking a more equitable and efficient research ecosystem, ultimately transitioning key players from the role of contributors to that of architectural leaders.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eThe Forthcoming Frontier: From Functional Mimicry to 4D Bioassembly\u003c/h2\u003e \u003cp\u003eHaving established the empirical impetus for a new paradigm from our bibliometric data, we now define and substantiate the concept of 4D Bioassembly. The empirical foundation for this shift is laid by the convergence of key enabling technologies. Foundational work on gene-activated matrices demonstrated the temporal programming of cellular fate [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Advances in nanomaterials enhanced the precision control over cellular microenvironments [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], while integrated 3D printing technologies enabled spatially orchestrated tissue formation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Most notably, smart biomaterials such as the aggrecanase-1-responsive hydrogel developed by Feng et al. exemplify systems capable of dynamic interaction with the pathological microenvironment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis technological convergence points unequivocally to a field outgrowing its current paradigm. While Functional Mimicry aims to recapitulate the native tissue\u0026rsquo;s complex structure at a given timepoint, the next logical and data-supported frontier is 4D Bioassembly\u0026mdash;the spatiotemporally programmed and self-evolving regeneration of living tissues. Critically, 4D Bioassembly transcends the concept of \u0026lsquo;3D\u0026thinsp;+\u0026thinsp;time\u0026rsquo; by introducing the capacity for active, dynamic adaptation post-implantation. The construct is not merely a pre-formed static scaffold that degrades over time, but an intelligent system designed to sense, interpret, and respond to evolving host microenvironmental cues through built-in feedback loops, thereby guiding the regenerative process with spatiotemporal precision.\u003c/p\u003e \u003cp\u003eThis ambition finds its fundamental rationale in developmental biology. The native osteochondral unit, particularly the growth plate, serves as the quintessential blueprint for such spatiotemporal organization. As elucidated by Lui et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], the mammalian growth plate operates through a precisely coordinated spatial and temporal regulation of gene expression, governing zonal differentiation and cellular maturation over time. This intrinsic biological program demonstrates that the functional regeneration of osteochondral tissues is inherently a 4D process. Our proposed paradigm, therefore, does not emerge from a vacuum but seeks to replicate this sophisticated native control through engineering means.\u003c/p\u003e \u003cp\u003eThe operational mechanism of this paradigm is illustrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). As shown, the 4D Bioassembly scaffold achieves functional regeneration by dynamically responding to key microenvironmental signals (e.g., low pH, aggrecanase-1). This responsiveness triggers the programmed, spatiotemporal release of bioactive factors (such as VEGF, TGF-β, and BMP-2) to orchestrate angiogenesis, chondrogenesis, and osteogenesis. This feedback-driven interaction is the cornerstone of the 4D Bioassembly concept, enabling a truly adaptive and biomimetic regeneration process.\u003c/p\u003e \u003cp\u003eCritically, the concept of 4D Bioassembly is further substantiated by strategies that recapitulate developmental processes. For instance, Mendes et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] engineered a living, bilayered implant that mimicked an immature osteochondral graft. Upon implantation, this construct not only integrated with the host tissue but also progressively matured over time, re-establishing a structured osteochondral unit with a distinct tidemark and subchondral bone plate. This exemplifies the core of 4D Bioassembly: the creation of a living implant that is spatiotemporally programmed to self-evolve into a mature, functional tissue after implantation. This evidence bridges our bibliometric findings with concrete experimental validation, confirming that the field is indeed transitioning from conceptual aspiration to tangible implementation.\u003c/p\u003e \u003cp\u003eIn summary, this study decodes osteochondral repair as a discipline propelled by a translational feedback loop, charted through a triple-paradigm evolution, and currently challenged by global collaboration asymmetry. The field\u0026rsquo;s trajectory is unequivocally defined by its progression into the Functional Mimicry paradigm and is now pivoting toward the ultimate frontier of 4D Bioassembly. Future breakthroughs, therefore, hinge on strategically bridging collaboration network disparities and fostering deeper interdisciplinary integration to achieve the spatiotemporally programmed regeneration of fully functional osteochondral tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 4D Bioassembly scaffold achieves functional regeneration by dynamically responding to key microenvironmental signals (e.g., low pH, aggrecanase-1) to trigger the programmed release of bioactive factors (VEGF, TGF-β, BMP-2) for angiogenesis, chondrogenesis, and osteogenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLimitations and Future Prospects\u003c/h2\u003e \u003cp\u003eThis study has several inherent limitations that should be considered. The exclusive reliance on the Web of Science Core Collection for literature sourcing may introduce a selection bias, as relevant publications in other databases or in non-English journals were not included. Furthermore, as a macro-trend analytical tool, bibliometrics excels at mapping the knowledge landscape but cannot assess the methodological rigor or the risk of bias within individual primary studies\u0026mdash;a critical task that remains the domain of systematic reviews.\u003c/p\u003e \u003cp\u003eGuided by the findings of this analysis, we propose three strategic directions for future endeavors to accelerate the field\u0026rsquo;s advancement:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDeepening Interdisciplinary Integration\u003c/b\u003e: Future progress hinges on forming deeply integrated teams that combine expertise from materials science, developmental biology, and clinical medicine. Such collaboration is essential to tackle the core challenges of \u0026ldquo;functionalized regeneration,\u0026rdquo; including achieving robust neurovascular integration and ensuring long-term biomechanical compatibility, thereby strengthening the translational feedback loop.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOptimizing Global Collaboration Networks\u003c/b\u003e: There is a pressing need to encourage strategic partnerships between high-output research institutions in emerging countries and the core hubs within the global innovation network. This initiative is critical to address the identified global research asymmetry, helping to bridge the \u0026ldquo;centrality gap\u0026rdquo; and promote a more balanced and efficient flow of knowledge and talent.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBridging the Translational Pathway for 4D Bioassembly\u003c/b\u003e: To realize the potential of the 4D Bioassembly paradigm, the field must focus on standardizing biofabrication protocols, conducting rigorously controlled large-scale clinical trials, and implementing comprehensive long-term outcome assessments to evaluate the safety, efficacy, and durability of the regenerated osteochondral tissues.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang J, Jing X, Wang Z, Liu X, Zhu X, Lei T et al (2021) In vitro and in vivo study on an injectable glycol chitosan/dibenzaldehyde-terminated polyethylene glycol hydrogel in repairing articular cartilage defects. 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Int J Mol Sci 22:11215\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Lu J, Song Y, Zeng C, Wang Y, Yang C et al (2022) Repair of bone defects in rhesus monkeys with alpha1,3-galactosyltransferase-knockout pig cancellous bone. Front Bioeng Biotechnol 10:990769\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasiliadis HS, Danielson B, Ljungberg M, McKeon B, Lindahl A, Peterson L (2010) Autologous chondrocyte implantation in cartilage lesions of the knee: long-term evaluation with magnetic resonance imaging and delayed gadolinium-enhanced magnetic resonance imaging technique. Am J Sports Med 38:943\u0026ndash;949\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMistry H, Connock M, Pink J, Shyangdan D, Clar C, Royle P et al (2017) Autologous chondrocyte implantation in the knee: systematic review and economic evaluation. 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Health Technol Assess 9:iii\u0026ndash;iv ix-x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Glynne-Jones P, Andriotis OG, Ching KY, Jonnalagadda US, Oreffo RO et al (2014) Application of an acoustofluidic perfusion bioreactor for cartilage tissue engineering. Lab Chip 14:4475\u0026ndash;4485\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHangody L, Fules P (2003) Autologous osteochondral mosaicplasty for the treatment of full-thickness defects of weight-bearing joints: ten years of experimental and clinical experience. J Bone Joint Surg Am 85\u0026ndash;A(Suppl 2):25\u0026ndash;32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamp CL, Barlow JD, Krych AJ (2015) Transplantation of a tibial osteochondral allograft to restore a large glenoid osteochondral defect. Orthopedics 38:e147e152\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorrie AM, Kesler WW, Elkin J, Gallo RA (2015) Osteochondral allograft. Curr Rev Musculoskelet Med 8:413\u0026ndash;422\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang T, Xu W, Zhao X, Bai B, Hua Y, Tang J et al (2022) Repair of osteochondral defects mediated by double-layer scaffolds with natural osteochondral-biomimetic microenvironment and interface. Mater Today Bio 14:100234\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang H, Zeng Y, Varghese S (2018) Functionally graded multilayer scaffolds for in vivo osteochondral tissue engineering. Acta Biomater 78:365\u0026ndash;377\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMin Q, Yu X, Liu J, Wu J, Wan Y (2019) Chitosan-based hydrogels embedded with hyaluronic acid complex nanoparticles for controlled delivery of bone morphogenetic protein-2. Pharmaceutics 11:214\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelardas O, Giannos P (2022) How COVID-19 affected the journal impact factor of high impact medical journals: bibliometric analysis. J Med Internet Res 24:e43089\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLunny C, Neelakant T, Chen A, Shinger G, Stevens A, Tasnim S et al (2022) Bibliometric study of \u0026lsquo;overviews of systematic reviews\u0026rsquo; of health interventions: evaluation of prevalence, citation and journal impact factor. Res Synth Methods 13:109\u0026ndash;120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGudas R, Kalesinskas RJ, Kimtys V, Stankevicius E, Toliusis V, Bernotavicius G et al (2005) A prospective randomized clinical study of mosaic osteochondral autologous transplantation versus microfracture for the treatment of osteochondral defects in the knee joint in young athletes. Arthroscopy 21:1066\u0026ndash;1075\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrittberg M, Lindahl A, Nilsson A, Ohlsson C, Isaksson O, Peterson L (1994) Treatment of deep cartilage defects in the knee with autologous chondrocyte transplantation. N Engl J Med 331:889\u0026ndash;895\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrittberg M, Gomoll AH, Canseco JA, Far J, Lind M, Hui J (2016) Cartilage repair in the degenerative ageing knee. Acta Orthop 87(sup363):26\u0026ndash;38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunziker EB (2002) Articular cartilage repair: basic science and clinical progress. A review of the current status and prospects. Osteoarthritis Cartilage 10:432\u0026ndash;463\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapiro F, Koide S, Glimcher MJ (1993) Cell origin and differentiation in the repair of full-thickness defects of articular cartilage. J Bone Joint Surg Am 75:532\u0026ndash;553\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao E, Yaszemski M, Krebsbach P, Hollister S (2007) Tissue-engineered cartilage constructs using composite hyaluronic acid/collagen I hydrogels and designed poly(propylene fumarate) scaffolds. Tissue Eng 13:537\u0026ndash;550\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMithoefer K, Williams RJ 3rd, Warren RF, Potter HG, Spock CR, Jones EC et al (2005) The microfracture technique for the treatment of articular cartilage lesions in the knee. A prospective cohort study. J Bone Joint Surg Am 87:1911\u0026ndash;1920\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarx RE, Carlson ER, Eichstaedt RM, Schimmele SR, Strauss JE, Georgeff KR (1998) Platelet-rich plasma: growth factor enhancement for bone grafts. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 85:638\u0026ndash;646\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeedham CJ, Shah SR, Dahlin RL, Kinard LA, Lam J, Watson BM et al (2014) Osteochondral tissue regeneration through polymeric delivery of DNA encoding for the SOX trio and RUNX2. Acta Biomater 10:4103\u0026ndash;4112\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEftekhari A, Maleki Dizaj S, Sharifi S, Salatin S, Rahbar Saadat Y, Zununi Vahed S et al (2020) The use of nanomaterials in tissue engineering for cartilage regeneration; current approaches and future perspectives. Int J Mol Sci 21:536\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiloksumpan P, de Ruijter M, Castilho M, Gbureck U, Vermonden T, van Weeren PR et al (2020) Combining multi-scale 3D printing technologies to engineer reinforced hydrogel-ceramic interfaces. Biofabrication 12:025014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng X, Zhou T, Xu P, Ye J, Gou Z, Gao C (2020) Enhanced regeneration of osteochondral defects by using an aggrecanase-1 responsively degradable and N-cadherin mimetic peptide-conjugated hydrogel loaded with BMSCs. Biomater Sci 8:2212\u0026ndash;2226\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLui JC, Andrade AC, Forcinito P, Hegde A, Chen W, Baron J et al (2010) Spatial and temporal regulation of gene expression in the mammalian growth plate. Bone 46:1380\u0026ndash;1390\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendes LF, Bosmans K, Van Hoven I, Viseu SR, Marechal M, Luyten FP (2020) Developmental engineering of living implants for deep osteochondral joint surface defects. Bone 139:115520\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bibliometrics, Osteochondral Repair, Tissue Engineering, 3D Printing, Regenerative Medicine","lastPublishedDoi":"10.21203/rs.3.rs-8681412/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8681412/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eResearch in osteochondral repair has evolved exponentially from surgical techniques to regenerative medicine. While bibliometrics can map this expansion, a deeper synthesis is needed to uncover the underlying dynamics and future paradigms that will strategically guide the field.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eA systematic search of the Web of Science Core Collection (2000\u0026ndash;2024) identified 2,919 publications for analysis. VOSviewer (v1.6.20) constructed co-occurrence and co-citation networks, while CiteSpace (v6.4.R1) was employed for burst detection, dual-map overlays, collaboration analysis, and timezone views to reveal evolution pathways.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eOur analysis reveals a mature, tripartite intellectual structure and a robust bidirectional knowledge flow, forming a \u0026ldquo;translational closed-loop.\u0026rdquo; We document significant global research asymmetry and conceptualize the field\u0026rsquo;s evolution as three sequential paradigms: 1) Structural Repair (c. 2000\u0026ndash;2010); 2) Biological Regeneration (c. 2011\u0026ndash;2017); and 3) Functional Mimicry (2018\u0026ndash;present). Critically, our data identify the convergence of sustained citation bursts in \u0026ldquo;osteochondral regeneration\u0026rdquo; (strength: 19.93), \u0026ldquo;3D printing,\u0026rdquo; and \u0026ldquo;hydrogel\u0026rdquo; as the empirical foundation for a new, emerging paradigm: 4D Bioassembly (spatiotemporally programmed and self-evolving regeneration of living tissues).\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eWe conclude that the field\u0026rsquo;s trajectory is defined by its progression into the Functional Mimicry paradigm and is now pivoting toward the ultimate frontier of 4D Bioassembly.\u003c/p\u003e","manuscriptTitle":"4D Bioassembly: Evolutionary Pathway and Future of Osteochondral Repair via Bibliometric Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 06:51:50","doi":"10.21203/rs.3.rs-8681412/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a83d8444-d04f-4881-8e8f-7a3d337511f4","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61662549,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2026-01-27T06:51:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 06:51:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8681412","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8681412","identity":"rs-8681412","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}