Global Trends of Mechanical Thrombectomy in Acute Ischemic Stroke Over the Past Decade: A Scientometric Analysis Based on WOSCC and GBD Database
Bibliographic record
Abstract
OBJECTIVE: To present a global overview of the current research landscape and emerging trends in mechanical thrombectomy for acute ischemic stroke (AIS) over the past decade. METHODS: A thorough search was conducted on the Web of Science on May 20, 2024, focusing on original articles and reviews in English. Bibliometric tools were employed to make a network analysis and visual representation. Additionally, data on disability-adjusted life years, prevalence, and incidence of ischemic strokes were extracted from the Global Burden of Disease database. RESULTS: A total of 7776 papers were included, indicating a steady increase from 169 to 1311 between 2014 and 2023. The United States led in core publications with 2887 papers. The incidence and disability-adjusted life years of ischemic stroke have continued to rise in Asia but have recently declined in North America and European countries. The University of Calgary emerged as the leading institution and Mayank Goyal was the most prolific author. Neurointerventional Surgery was the top contributing journal with 790 articles. The analysis identified 6332 keywords forming 5 clusters, with "mechanical thrombectomy" serving as the largest cluster, focusing mainly on interventional thrombectomy techniques for AIS. The term "tissue plasminogen activator" exhibited strong burst strength of 46.58. Keywords such as "injury", "diagnosis", "posterior circulation", and "severity" burst in 2020 and lasted until 2024. CONCLUSIONS: Interest in mechanical thrombectomy for AIS was progressively increasing. Future research directions may include minimizing intraoperative injuries, refining diagnostic techniques, investigating interventions for posterior circulation, and tailoring thrombectomy strategies based on stroke severity and large vessel occlusion etiology.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.145 | 0.180 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".