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Though currently considered a 'black box,' machine learning (ML) has a promising future to ameliorate the health-care burden of stroke which is the second leading cause of mortality worldwide. Through this study, we sought to review the most influential articles on the applications of ML in stroke. Web of Sciences database was searched, and a list of the top 50 most cited articles, assessing the application of ML in stroke, was prepared by 2 authors, independently. Subsequently, a detailed analysis was performed to characterize the most impactful studies. The total number of citations to the top 50 articles were 2959 (range 35–243 citations) with a median of 47 citations. Highest number of articles were published in the journal Stroke and the United States was the major contributing country. The majority of the studies focused on the utilization of ML to improve stroke risk prediction, diagnosis, and outcome prediction. Statistical analysis revealed an insignificant association between the total and mean number of citations and the impact factor of the journal (P = 0.516 and 0.987, respectively). Recent years have witnessed a surge in the application of ML in stroke, with an enhancement in interest and funding over the years. ML has revolutionized the management of stroke and continues to aid in the neurosurgical decision-making and care in stroke patients. [ABSTRACT FROM AUTHOR]

The purpose of our bibliometric analysis was to explore the disciplinary hotspots and collaborative networks in research on neuropathic pain (NPP) during in the past 20 years. The studies related to NPP were obtained from Web of Science database. Global publications on NPP were analyzed for different factors, including the number of reports, citation rates, authors, institutions, countries and regions, journals, funding, and relevant partnerships and topic hotspots. From 2001 to 2020, 6905 studies related to NPP research were reported worldwide. The number of publications had increased continually during the past 20 years. Pain was the most productive and most frequently co-cited journal. Ralf Baron was the most productive and influential author. The most productive country and institution were the United States and Harvard University, respectively. Investigators and institutions from the United States, Japan, and China were the core research forces. We found broad and close cooperation in the field worldwide. The top authors and top institutions had collaborated relatively closely with others. The research of NPP is a well-developed and prospective field of medical study. The journals Pain, European Journal of Pain, and Molecular Pain showed more interested in this field. The United States, Harvard University, and Ralf Baron were the top country, institution, and author, respectively. Global research collaboration was extensive, with the top institutions and authors cooperating with others. [ABSTRACT FROM AUTHOR]

Gliomas are the most common primary malignant tumor of the central nervous system. At the present time, radiotherapy (RT) is the cornerstone of therapeutic approaches, but the worldwide research trends in RT for gliomas in the world are unclear. This bibliometric analysis aimed to investigate trends in glioma RT research since 2011. The Web of Science database was searched, and SigmaPlot 14.0 and VOSviewer software were used to analyze and visualize the trends. Bibliometric analysis of 5106 articles was conducted. While the number of publications per year was relatively steady, the number of citations grew exponentially from 356 in 2011 to 21,051 in 2020. Articles originated from 89 countries, with the majority from the United States. Journal of Neuro-Oncology and Texas A&M University were the journal and institution, respectively, with the highest number of publications. The articles mainly involved oncology, neurology, radiology, and surgery. This bibliometric analysis found an increased focus on glioblastoma, survival, and chemotherapy in glioma RT research. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to augment clinicians' diagnostic and decision-making capabilities. It is well suited to identify patterns and correlations within data sets and may be applied to identify elements of importance in complex and data-laden areas such as patient selection, diagnostics, treatment, and outcome prediction. The development of modern neurosurgery has been dependent on major technological advances. In line with this, a growing interest is seen in the use of AI to assist in neurosurgical research and enhance neurosurgical practices. A bibliometric analysis of the 50 most-cited articles alluding to the use of AI in neurosurgery, from inception until July of 2022, was undertaken using the Web of Science database. Statistical analyses were performed on R. The citation count ranged from 29 to 159 (mean: 51.9, standard deviation: 24.8), and the top-cited article was a 2018 systematic review published in World Neurosurgery. Most articles were published after 2015 (85%). The United States was the largest contributing country on the list with 22 articles. Four first and last authors, each, had 2 or more publications. Female first and last authorship was attributed to 18% and 0% of the articles, respectively. This review highlights the most-impactful articles pertaining to AI in the field of neurosurgery. Although female authors were significantly underrepresented on the list, their work was at least as impactful as their male peers. Finally, the striking dominance of articles originating from the developed world raises concerns as to the future of AI in attending to the global health crisis. [ABSTRACT FROM AUTHOR]

Extracranial-intracranial (EC-IC) bypass is a procedure in which a blood vessel outside the skull is connected to one inside the skull to improve cerebral blood flow. Certain aneurysms cannot be treated through standard clipping, and EC-IC bypass may be recommended in such cases. A vast body of literature exists on the use of EC-IC bypass as a treatment for cerebrovascular disorders. While beneficial to surgeons, these publications may not reach all the intended audience, which encourages the use of bibliometric analyses. Although a fraction of historically meaningful publications may not have been sufficiently valued through citation count, bibliometric analysis is the gold standard for assessing the impact of a publication. Using bibliometric analysis, we queried the Web of Science database to identify the 50 most impactful publications on EC-IC bypass based on citation count. The literature search retrieved 125 publications that met inclusion criteria, from which the top 50 most-cited ones were selected. The mean number of citations for each article was 117.06 (range 35–1437). Case reports and series constituted 15 (30%) of the top 50 publications on EC-IC bypass, followed by 8 (16%) editorials and 7 (14%) randomized controlled trials. A total of 265 authors in 10 countries contributed to the 50 manuscripts, which were published in 15 different journals. We retrieved the top 50 most-cited articles on EC-IC bypass surgery and identified the landmark publications to provide a foundational understanding of the procedure as a treatment for complex disorders. [ABSTRACT FROM AUTHOR]