Current Application Status and Trends in Paravertebral Block for Thoracic Surgery: A 2004–2024 Bibliometric Analysis
Bibliographic record
Abstract
Aims: To elucidate the current application status and research trends of paravertebral block (PVB) regional anesthesia in thoracic surgery. Methodology: Using bibliometric methods, we analyzed 931 publications from Web of Science (2004-2024) with CiteSpace 6.2.R4 to map knowledge networks and evolving trends in paravertebral block for thoracic surgery. Visual knowledge mapping was employed to identify core researchers, research hotspots, and keyword clustering in thoracic PVB applications. Results: Research output demonstrated significant growth over the past decade. Visualization analysis reveals that Canada and the United States dominated the field's intellectual development. While inter-institutional collaboration was active, overall research cohesion remained suboptimal. PVB research primarily focused on pain management and anesthesia protocol optimization, with high-centrality keywords including pain, anesthesia, postoperative pain, surgery and analgesia. Emerging trends revealed a shift from traditional agent toward minimally invasive techniques and novel nerve blocks. Conclusion: PVB exhibits significant analgesic efficacy in thoracic procedures. Future research prioritizes continuous paravertebral block and multimodal analgesia protocols. PVB holds substantial promise for postoperative analgesia and enhanced recovery pathways, with AI-assisted protocols potentially optimizing clinical implementation. Strengthening multinational and cross-institutional collaboration is essential to advance research synergy.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.100 | 0.177 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".