Regional Anesthesia (2012–2021): A Comprehensive Examination Based on Bibliometric Analyses of Hotpots, Knowledge Structure and Intellectual Dynamics
Bibliographic record
Abstract
Abdullah Shbeer College of Medicine, Jazan University, Jazan, Saudi ArabiaCorrespondence: Abdullah Shbeer, College of Medicine, Jazan University, Jazan, Saudi Arabia, Tel +966505769570, Email Ashbeer@jazanu.edu.saAbstract: In the last decade, there has been a significant advancement in the area of regional anesthesia (RA). Continuous evaluation of research in any developing field using modern technologies and available software is critical to identify future trends, hot spots, and intellectual dynamics. The current study was designed to bibliometrically evaluate the global research in RA using VOSviewer, MS Excel, and CVS-Scopus bibliographic data (2012– 2021). Knowledge structure and intellectual dynamics were analyzed using clustering of keyword co-occurrence. Literature screening in the last decade found 6092 original articles (96.1%) and conference papers (3.9%). The top four countries producing articles were the United States (n = 30.57%), India (7.51), the United Kingdom (7.22%), and Canada (6.06%). A significant positive correlation was found in global publication productivity (R2 = 0.9161). The most productive organizations were Harvard University, the University of Toronto, and the Hospital for Special Surgery – New York. A tremendous collaboration was spotted nationally and internationally, especially in pediatric RA. This comprehensive study, which summarizes and evaluates 6902 original research materials on regional anesthesia, may serve as a resource for anesthesiologists, physicians, researchers, and students.Keywords: regional anesthesia, bibliometrics, VOSviewer, knowledge structure, intellectual dynamics
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.134 | 0.149 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".