MétaCan
Menu
Back to cohort
Record W7036980550

Current Status of Research on Tuina for Analgesia: A Bibliometric and Visual Analysis

2023· article· en· W7036980550 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
Fundersnot available
KeywordsBeijingBibliometricsChinaWeb of scienceAcupunctureAlternative medicineCitation
DOInot available

Abstract

fetched live from OpenAlex

Zheng Wang,1,* Hui Xu,1,2,* Hang Zhou,1 Wanyu Li,1 Tao Yang,1 Yunfeng Zhou1 1Henan University of Chinese Medicine, Zhengzhou, 450046, People’s Republic of China; 2Tuina Department, the Third Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450046, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yunfeng Zhou, College of Acupuncture and Massage, Henan University of Chinese Medicine, No. 156, Jinshui East Road, Zhengzhou, Henan Province, People’s Republic of China, Tel +8615639717497, Fax +86037165962633, Email zyf5680198@126.comPurpose: 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.Keywords: analgesia, tuina, bibliometric analysis, VOSviewer, CiteSpace

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.2360.275
Science and technology studies0.0020.001
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.687
GPT teacher head0.707
Teacher spread0.019 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicResearch Data Management PracticesFrench-language works237,207