Scientific Knowledge Graph of Acupuncture for Migraine: A Bibliometric Analysis from 2000 to 2019
Bibliographic record
Abstract
OBJECTIVE: This study aims to explore the trend and knowledge mapping of acupuncture for migraine through bibliometrics. METHODS: It retrieved the literature on acupuncture for migraine in the Web of Science database from 2000 to 2019, and then resorted to CiteSpace to conduct bibliometric analysis to attain the knowledge mapping. RESULTS: The total number of publications each year has increased year by year, and the average annual growth rate from 2000 to 2009 was 15.57%, while from 2010 to 2019 was 6.35%, with a faster growth rate from 2000 to 2009. According to the cluster analysis of institutions, authors, cited references, and keywords, 10, 7, 12, and 10 categories were gained from 2000 to 2019. The most productive countries, institutions, and authors are the USA and China, Technical University of Munich and Beijing University of Chinese Medicine, Linde K and Liang FR from 2000 to 2019, whose frequency is 119/103, 28/24, and 28/24, respectively. However, the most important of them are Canada, Sichuan University, and Witt CM. Owing to their highest centrality, they are 0.86, 0.54, and 0.27 separately. Moreover, cited references that contributed to the most co-citations are Linde K (2005), yet, the most vital cited reference is Karst M (2001). Keywords such as migraine, acupuncture, headache, pain, and randomized controlled trial are the most frequently used. However, needle acupuncture is the crucial keyword. In the cluster analysis of institutions, authors, cited references, and keywords from 2000 to 2019, the largest cluster categories are #0 migraine prophylaxis, #1 randomized controlled trial, #0 episodic migraine, and #0 topiramate treatment. Then, randomized controlled trials of acupuncture prevention and treatment of migraine are the most important research content in this field. CONCLUSION: Through the bibliometric analysis of the research on acupuncture for migraine in the Web of Science database in the past 20 years, the trends and the Knowledge Graph of the country, institution, author, cited reference, and the keyword are acquired, which have an important guiding significance for quickly and accurately positioning the key information in the field.
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How this classification was reachedexpand
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.096 | 0.203 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".