Bibliometric Analysis of Post-Stroke Pain Research Published from 2012 to 2021
Bibliographic record
Abstract
Feng Xiong,1 Peng Shen,1 Zhenhui Li,2 Ziyi Huang,1 Ying Liang,1 Xiwen Chen,1 Yutong Li,3 Xinping Chai,3 Zhen Feng,1,* Moyi Li1,* 1Rehabilitation Medicine Department, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China; 2Children Health Care Department, Longyan First Hospital Affiliated to Fujian Medical University, Longyan, People’s Republic of China; 3First School of Clinical Medicine, Nanchang University, Nanchang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Moyi Li; Zhen Feng, Rehabilitation Medicine Department, The First Affiliated Hospital of Nanchang University, No. 17, Yong Wai Zheng Jie, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86 15806031050 ; +86 13970038111, Email limoyi123@aliyun.com; fengzhen@email.ncu.edu.cnBackground and Purpose: Pain is one of the most common symptoms in patients after stroke. It is a distressing experience that affects patients’ quality of life, and it is highly prevalent in clinical practice. The pathogenesis mechanisms of PSP are not so clear, and there is currently a lack of effective medical treatments, hence it is necessary to establish a sufficient understanding of this disease. Limited number of studies have applied bibliometric methods to systematically analyze studies on post-stroke pain. This study aimed to systematically analyze scientific studies conducted worldwide on post-stroke pain from 2012 to 2021 to evaluate global trends in this field using a bibliometric analysis.Methods: Publications related to post-stroke pain from 2012 to 2021 were obtained from the Web of Science Core Collection database. Bibliometrics Biblioshiny R-package software was used to analyze the relationship of publication year with country, institution, journals, authors, and keywords and to generate variant visual maps to show annual publications, most relevant countries, authors, sources, keywords, and top-cited articles.Results: In this study, 5484 papers met the inclusion criteria. The annual growth rate of publications was 5.13%. The USA had the highest number of publications (1381, 25.2%) and citations (36,395), and the University of Toronto had the highest number of papers (156, 2.8%). “Stroke”, “management”, “pain”, “risk”, “prevalence”, “ischemic stroke”, “risk factors”, “disease”, “diagnosis” and “therapy” are the top 10 keywords.Conclusion: The global research interest regarding PSP has maintained growing over the past ten years. Both central post stroke pain and hemiplegic shoulder pain are the hottest research subjects. Further investigations are needed in order to reveal the mystery of the pathophysiologic mechanisms of CPSP, and high-quality well-designed trials of potential treatments of CPSP and HSP are also needed.Keywords: post-stroke pain, publication trends, bibliometric analysis, research interest
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.123 | 0.220 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".