Current Status of Research on Tuina for Analgesia: A Bibliometric and Visual Analysis
Bibliographic record
Abstract
Purpose: Tuina is a nonpharmacological modality for pain relief that has found applications in the treatment of several pain disorders. Tuina analgesia has been increasingly studied; however, few studies have focused on the previous publication trends, prevalent research areas, collaborations, and other factors. This study aimed to systematically analyze research trends and hot topics in the field of tuina analgesia over the past 30 years, using bibliometric analysis, to inform future research. Methods: The web of science database was searched for literature on tuina analgesia from 1992-2023. VOSviewer and CiteSpace were used to analyze annual publication volumes, countries, institutions, journals and CO-cited journals, authorship, articles, and keywords and their relevance, and to perform co-occurrence and clustering analyses. Results: A total of 621 literature elements were included in the analysis. The annual volume of publications has increased steadily in recent years. The top three high-yielding countries were the United States, China, and Canada, respectively. The top three institutional outputs were from Shanghai University of Chinese medicine, Beijing University of Chinese medicine, and McMaster University, respectively. Notably, there was an imbalance between national outputs and centrality, with higher centrality in the United States (0.35) and lower in China (0.01). Cochrane Database of Systematic Reviews was the journal with the most publications (22), and PAIN was the most influential co-cited journals (publications=306). Moreover, current research in this field was dominated by studies on Tuina for relieving postoperative pain, the effectiveness of Tuina analgesia, and Tuina treatment for pain accompanied by anxiety. Conclusion: This study employed bibliometrics to analyze the literature on Tuina for pain treatment over a 30-year period, identifying potential collaborators, institutions, hot topics, and future research trends that will inform potential future directions.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.223 | 0.281 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".