Bibliometrics and knowledge map analysis of ultrasound-guided regional anesthesia
Bibliographic record
Abstract
Through bibliometric analysis, we aim to comprehensively understand the research dynamics in this field, reveal key scientific research achievements and breakthrough discoveries, and provide valuable reference and guidance for future research directions. Utilizing the Web of Science, we retrieved the literature pertaining to ultrasonics-guided regional anesthesiology (1994-2022). CiteSpace and VOSviewer were used for bibliometric and knowledge mapping analysis. Our examination encompassed publication trends, authorship patterns, institutional contributions, frequently occurring keywords, keyword clustering, and emerging terminology trends. Of the 570 papers reviewed, there was a rising trend in publications each year. The main keywords in regional anesthesia were ultrasound guidance, nerve, analgesia, and pain score. Key research areas were regional anesthesia, ultrasound guidance, approach, pain score, and plane block. The U.S. led in research. Stanford University, University of Toronto, and Cork University Hospital were central institutions. Chan V was the top author with 24 articles, while Marhofer P was the most cited at 150 times. Regional anesthesia and pain medicine were the predominant journal in both publications and citations. In conclusion, research in this field consistently grew yearly, and visualization showcased trends in ultrasound-guided regional anesthesia. These visuals provided key bibliometric insights, helping researchers further explore and understand this domain.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.177 | 0.198 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".