Bibliometric Analysis: Research Trends and Performances of Stroke on Acupuncture
Bibliographic record
Abstract
Purpose: This study aimed to apply the bibliometric analysis to summarize acupuncture therapy for stroke, demonstrated and evaluated the trends, major research hotspots and frontier areas. Materials and Methods: Articles on acupuncture for stroke were selected from the Web of Science Core Collection (WoSCC) from the inception of the database up until 2023. CiteSpace software was performed to conduct the collaborative analysis of networks of countries, institutions, authors and cited authors, journals and cited journals, cited references, keywords clustering and burstiness analysis. Results: A total of 1141 articles were retrieved. China was the most productive country (851) and had the greatest centrality (0.43). Beijing Univ Chinese Med (86) contributed to the most publications. Chen LD (31) and Tao J (31) were the most prolific authors, of which all from Fujian Univ Tradit Chinese Med. Wu P (124) from Canadian College of Naturopathic Medicine, Canada, was the most cited author. Evidence-based Complementary and Alternative Medicine (89) was the most productive journal, while Stroke (744) was first cited journals. Stimulation, recovery, ischemic stroke, electroacupuncture, rehabilitation were the most high-frequency keywords. Future research in this area will pay more attention to the evaluation of the effectiveness of acupuncture therapeutics in treating stroke, conducting the clinical research on cognitive ability, quality of life and partial function of stroke patients, and basic research related to mechanisms. Conclusion: The publications on acupuncture in stroke have shown major development, but the international cooperation for academic exchange among researchers and institutions remained to be strengthened to promote interdisciplinary and academic innovation. Furthermore, except for the molecular mechanism of acupuncture in treating functional rehabilitation of stroke, exploring the more high-quality clinical studies may become a key point based on the evidence-based medicine.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.097 | 0.126 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".