Impact of COVID-19 on Stroke: A Scientometric Assessment of Global Publications during 2020-21
Bibliographic record
Abstract
Background and Aim: The COVID-19, as a global epidemic, has led to public health problems including its impact on stroke. There has been a significant increase in the research on the impact of COVID-19 on stroke. Little attention, however, has been focused on the overall trend in this field based on bibliometric analysis. This study aimed to assess the characteristics and trends of research on “Impact of COVID-19 on Stroke”. Methods: Using Scopus database, a search query was formulated involving keywords related to “COVID-19” and “Stroke” for identifying relevant literature on this topic, resulting in 956 publications during 2020- 21. Results: The 956 global publications on this topic registered an average 11.75 citations impact per paper. About one-fifth (19.56%) share of global publications was supported by extramural funding support. USA (n=341; 35.67%) contributed the largest number of papers, followed by the Italy (n=106; 11.09%) and U.K. (n=77; 8.05%). China (24.42 and 2.52) had registered the highest citation impact per paper and relative citation index, followed by France (22.02 and 1.87) and the USA (18.96 and 1.61). Harvard Medical School, USA (n=27) ranked first in publication productivity, followed by the University of Toronto, Canada (n=23) and The University of Thessaly, Greece (n=23). NTU Langone Health, USA (54.27 and 4.62) University of Pennsylvania, USA (35.55 and 3.03) and Hospital Universitario La Paz, Spain (28.0 and 2.38) ranked top in citation impact. S. Yagi (USA) (n=16) ranked first in publication productivity, followed by G. Tsivgoulis (Greece) (n=15) and H.S. Markus (U.K.) (n=11) and J.T. Fifi (USA)(171.88 and 14.63), J. Mocco (USA)(128.45 and 10.93) and P. Jabbour (USA)(45.86 and 3.90) ranked on top in citation impact. Stroke (n=95) ranked first in productivity, followed by Journal of Stroke and Cerebrovascular Disease (n=81) and Frontiers in Neurology (n=34). Conclusion: This study suggests that a large amount of literature has accumulated on “Impact of Covid-19 on Stroke”, both from developed and developing countries.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.099 | 0.138 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".