Visualization analysis of thoracic paravertebral block in breast surgery based on bibliometrics
Bibliographic record
Abstract
Background: Breast surgery is one of the most common surgeries in the world, and pain after breast surgery is very common, representing one of the key factors affecting the quality of life after surgery. With the development of clinical techniques, thoracic paravertebral block (TPVB) has gradually become the preferred regional anesthesia technique for postoperative breast analgesia. Methods: Using Web of Science as the data source, medical articles about the application of TPVB in breast surgery published from 1900 to 2022 were retrieved and imported into CiteSpace and VOSviewer software. Using bibliometrics and knowledge mapping visualization methods, the literature was analyzed from the aspects of publication, author, institution, country, high-frequency keywords, keyword clustering, emergence words, and so on. Results: A total of 299 articles were included. according to the yearly numbers of articles, the trend is increasing annually. The most published authors in this field are Susan M. Steele, Roy A. Greengrass, Brian M. Ilfeld, Karmakar Manoj Kumar, and Stephen M. Klein. The League of European Research Universities, University of Toronto, and Duke University are the 3 institutions with the largest number of publications, and their cooperation degree is relatively low. Articles of American origin predominated. TPVB is the major keyword associated with the application of TPVB in breast surgery, which appears most frequently and has a high research interest. Conclusions: The trend and characteristics of TPVB application research in breast surgery were visualized, and the studies in this field are generally increasing annually in number, providing useful bibliometric analysis for researchers to further explore in this field.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.151 | 0.134 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".