Global trends in perioperative stroke research from 2003 to 2022: a web of science-based bibliometric and visual analysis
Bibliographic record
Abstract
Background: Perioperative stroke is a potentially devastating complication in surgical patients, which has attracted global attention. This retrospective bibliometric and visual analysis evaluates the status and global trends in perioperative stroke research. Methods: Papers published between 2003 and 2022 were retrieved from the Web of Science core collection. Extracted data were summarized and analyzed using Microsoft Excel and further bibliometric and co-occurrence analyses were conducted using VOSviewer and CiteSpace software. Results: Publications on perioperative stroke have increased over the years. The USA topped the list of countries with the highest number of publications and citations, while Canada had the highest mean citation frequency. The Journal of Vascular Surgery and Annals of Thoracic Surgery had the highest number of publications and citation frequency for perioperative stroke. Regarding authors, Malas, Mahmoud B. contributed the most publications to the field, and Harvard University had the highest number of publications (409 papers). Based on an overlay visualization map, timeline view, and the strongest strength burst of keywords, "antiplatelet therapy," "antithrombotic therapy," "carotid revascularization," "bleeding complications," "postoperative cognitive dysfunction," "intraoperative hypotension," "thrombectomy," "cerebral revascularization," "valve surgery," "tranexamic acid," and "frozen elephant trunk" were trending topics in perioperative stroke research. Conclusion: Publications regarding perioperative stroke have experienced rapid growth in the past 20 years and are likely to continuously increase. Research on perioperative antiplatelet and antithrombotic, cardiovascular surgery, postoperative cognitive dysfunction, thrombectomy, tranexamic acid, and frozen elephant trunk has attracted increasing attention, and these topics are emerging hotspots of present research and possible candidates for future research.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.177 | 0.190 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".