Extended reality in acupuncture-related research and practice: A bibliometric analysis
Bibliographic record
Abstract
In recent years, the application of extended reality (XR) technology in acupuncture-related research and practice has gained increasing popularity. This article aimed to conduct a bibliometric analysis in this field. Publications between 1996 and 2024 in this field were searched in Web of Science Core Collection and Scopus. Software used for data preprocessing and analysis included Microsoft Excel, RStudio, CiteSpace, and VOSviewer. A total of 143 articles were selected. Publications in this field were increasing rapidly. The top three countries by publication volume were China, the United States and Canada. The top three most-cited countries were the United States, Belgium and China. The overall cooperation network was loose and mainly dominated by China, the United States, and the United Kingdom. The top three affiliations by publication volume were University of Toronto, Emory University, and Southern Medical University. KU Leuven had the highest total and average citations. The author with the most publications and total citations was Moseley G. The source with the most publications was Frontiers in Neurology, whereas the top cited source was Frontiers in Human Neuroscience. Hatem SM (Front Hum Neurosci, 2016) was the top cited publication with the highest average annual citation rate. The most frequent keywords included “virtual reality”, “acupuncture”, “cognitive behavioral therapy”, “stroke” and “pain”. Keyword clusters mainly focused on three aspects: neurological rehabilitation, mental health and pain management. An isolated sub-cluster existed in mental health cluster with “simulation” as its core keyword. As a specialized domain, the field is expected to progress by forging a more direct integration of XR with acupuncture and a deeper convergence of basic science and clinical practice, indicating substantial progress in the future. • XR use in acupuncture-related area has grown for nearly three decades. • While most studies kept XR and acupuncture separate, few integrated them directly. • Key research areas are neurorehabilitation, mental health, and pain management. • A mental health sub-cluster indicated a gap between technology and application.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.187 | 0.251 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".