Bibliometric Analysis of the Worldwide Scientific Production on COVID-19 Infection and Cerebrovascular Disease
Bibliographic record
Abstract
Objective: To identify the worldwide bibliometric characteristics of research on SARS-CoV-2 infection and cerebrovascular disease. Methods: A retrospective, descriptive, and bibliometric study was performed. We analyzed 1834 publications about COVID-19 and cerebrovascular disease from the Scopus database considering the time since the beginning of the pandemic between 2019 and 2020. Bibliometric indicators were evaluated such as number of citations, citations per publication by authors, countries, journals, and collaborations at national, international, institutional, and impact levels according to Cite Score Quartile and h-index metrics. All analysis was performed using SciVal software. Results: The highest percentage of articles corresponded to universities in the United States, including Harvard and New York with 59 and 20 publications, respectively, and the University of Toronto in Canada with 22 publications. In relation to citation indicators, journals such as Stroke and Journal Stroke and Cerebrovascular diseases obtained 1971 and 561 citations, respectively. Regarding collaboration indicators, the national collaboration index was 39.4% and the institutional collaboration index was 31.1%. Finally, neurology, cardiovascular medicine, and cardiology and surgery were the subject areas with the highest research results, with 424, 217, and 128 studies, respectively. Conclusion: It was observed that the United States was the country with the highest scientific production on COVID-19 and cerebrovascular disease in the year 2020 in the different health areas; however, more research is still needed worldwide for a better analysis of the bibliometric indicators on the subject.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.180 | 0.198 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".