Stroke ranks among the most prevalent diseases globally, posing a significant threat to life and 17 health. Despite variations in healthcare systems across nations, addressing the burden of stroke 18 remains a global priority (1). Bibliometric analysis is playing an increasingly vital role in medical 19 research, primarily due to its ability to quantitatively and qualitatively assess research trends and 20 hotspots within specific disciplines(2). This method efficiently analyzes the quantitative 21 characteristics and patterns of literature in stroke research, providing directional guidance and 22 reference for future studies. However, greater emphasis should be placed on methodological rigor 23 and scientific validity when conducting bibliometric research. 24We read with great interest on the publication by Liao Zhiping et al. (3) Second, the original manuscript presents contradictory narratives regarding author influence. In the 49 text, Lay B.S. is rated as the "most influential" author based on 120 total citations. However, Figure 50 5C in the same section visually represents author influence using the H-index, where Zhang X and 51Zhou P lead with an H-index of 11. The absence of a unifying framework in these statements, which 52 are based on disparate metrics, results in a state of perplexity. This represents a substantial deficiency 53 in the processes of data interpretation and reporting. Additionally, the study's evaluation of author 54 influence is insufficient and incomplete, relying solely on a limited set of metrics (publication 55 volume and H-index). We advocate that robust bibliometric assessments of author impact require 56 transparent, multi-metric approaches (e.g., total citations, average citation count, citations per paper, 57 H-index, G-index) rather than selective reporting. The original text's assertion about "most influential 58 authors" currently lacks substantiation and necessitates correction or more nuanced explanation. 59 Third, the original study's keyword analysis was limited to listing high-frequency terms like "stroke" 60 and "rehabilitation." This approach fails to reveal the field's dynamic knowledge structure beyond a 61 superficial snapshot. Specifically, we recommend the original authors conduct keywords co-62 occurrence network analysis and clustering to map distinct research sub-themes (e.g., "robot-assisted 63 therapy," "brain-computer interfaces," "muscle synergies"). Furthermore, analyzing thematic 64 evolution across different time periods is crucial for visualizing shifts in research focus (e.g., from 65 basic assessment to technology integration). Such deeper analysis transforms simple counts into 66 meaningful insights about past trends and future directions. 67The research indicates that future work should focus on clinical utility, but its discussion fails to 68 leverage the findings of bibliometrics to critically analyze specific barriers to clinical application. It is 69 recommended that the authors synthesize their results, including the prominence of engineering-70 focused, highly-cited papers versus patient-centered outcomes research, to structure a discussion on 71 translational challenges. These may include the lack of standardized sEMG protocols, cost-72 effectiveness studies, clinical practice guidelines, and interoperability with routine clinical 73 workflows. 74In summary, the authors' contributions to the study of surface electromyography trends in stroke 75 rehabilitation are acknowledged. It is further recommended that more rigorous research 76 methodologies and evaluation metrics be adopted to enhance the study's scientific rigor. 77
Liu et al. (Mon,) studied this question.