Clinical evidence in ischemic stroke: Where we have gone so far and hopes for the future
Bibliographic record
Abstract
OBJECTIVE: Ischemic stroke is a significant cause of disability and death worldwide. Randomized clinical trials (RCTs) are important in changing guidelines and treatment strategies. This study aimed to analyze the progress of RCTs in ischemic stroke and to guide future research directions. METHODS: Ischemic stroke-related RCT articles were identified in six high-impact medical journals using the Web of Science Core Collection database. Google Scholar was used to check whether relevant articles were included in the guidelines. The characteristics of these articles were analyzed and future research hotspots were predicted. RESULTS: 389 relevant articles were included in the analysis. The number of articles increased rapidly from 1972 to 2022, from 5 (1.3%; 1972-1982) to 208 (53.5%; 2013-2022) articles. 338 (86.9%) articles were included in relevant guidelines. According to corresponding author location, Europe was the source of the highest number of publications (183; 47.0%), followed by the Americas (152; 39.1%) and the Western Pacific (54; 13.9%). The number of publications steadily increased over time in the USA, England, Canada, Australia, Germany, and France, and surged in China and Spain, especially in the last 5 years. In recent years, endovascular therapy has accounted for the majority of ischemic stroke-related RCT articles. CONCLUSIONS: Numerous RCTs related to ischemic stroke have been conducted in recent decades, and both the number of articles and their contribution to guideline updates are increasing. Also, a shift in research topics was observed. However, great regional imbalances in this research exist, calling for more research to be conducted in specific regions to promote the generalizability of trial conclusions.
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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 | 0.101 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.022 | 0.040 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".