Commentary on “Evaluation of the comparative efficacy and safety of surgical strategies for long bone defects: a network meta-analysis”

Dear Editor, We read with interest the network meta-analysis by Shen et al, titled “Evaluation of the comparative efficacy and safety of surgical strategies for long bone defects,” which provides a valuable ranking of surgical interventions based on healing and complication rates[1]. The authors’ effort to synthesize both direct and indirect evidence in a field dominated by observational data is commendable. Our commentary was prepared in accordance with the TITAN 2025 guidelines for transparency in artificial intelligence reporting[2]. We wish to highlight two methodological aspects that could further refine the interpretation and clinical applicability of such network meta-analyses in orthopedic surgery. First, while the authors appropriately used surface under the cumulative ranking curve (SUCRA) values to rank treatments, the clinical meaningfulness of these rankings could be enhanced by incorporating probability of being the best treatment (PbBT) estimates alongside SUCRA. PbBT provides a more intuitive measure for clinicians by indicating the likelihood that a given treatment is superior to all others in the network[3]. This is particularly relevant in scenarios where SUCRA differences are modest, as seen between vascularized bone graft (VBG) and bone transport. Presenting both metrics would strengthen the practical utility of the findings. Second, the analysis highlights significant heterogeneity , which was partially addressed through subgroup analysis by defect size. However, other clinically relevant modifiers – such as bone location (tibia vs. femur), etiology (traumatic vs. infectious), and soft tissue status – were not explored. These factors are known to influence healing potential and complication profiles[4]. A more granular stratification or meta-regression adjusting for these covariates could reduce residual heterogeneity and improve the precision of treatment recommendations. For instance, VBG may perform differently in upper versus lower limb defects, a distinction not captured in the current analysis. Additionally, while the authors acknowledge the limitations of indirect comparisons, the use of node-splitting analysis to assess local inconsistency is a strength. We suggest that future updates of such analyses also incorporate comparative effectiveness plots to visually contextualize effect estimates against clinical minimally important differences for healing rates and complications. This would aid in translating statistical rankings into clinically actionable guidance. In summary, Shen et al have provided a useful synthesis of available evidence on long bone defect management. Enhancing the reporting with PbBT estimates and exploring additional clinical modifiers could further solidify the conclusions and support personalized surgical decision making. We encourage the authors to consider these refinements in future iterations of their work.