Bibliometric Analysis of Post-Stroke Pain Research Published from 2012 to 2021
Bibliographic record
Abstract
Background 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.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.104 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.348 | 0.350 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".