Covid-19 associated coagulopathy (CAC): Global research output, 2020-2022
Bibliographic record
Abstract
Objective. This study examined the Covid-19 associated coagulopathy (CAC) publication and citation trends, focusing on the top authors, countries, organizations, journals, collaboration, subject areas, keywords, and high-cited articles. Design/Methodology/Approach. Publications data on Covid-19 associated coagulopathy (CAC) were identified and extracted from the Scopus database. Data analysis has been performed using VOS viewer software, Biblioshiny, and MS Excel Software. Results/Discussion. The study identified 1740 articles on Covid-19 associated coagulopathy from the Scopus database, which received 47,502 citations, averaging 27.3 citations per paper. In all, 905 authors from 601 organizations originating in 96 countries participated in research and published in 776 journals. United States, Italy, and China contributed the most publications (498, 258, and 164 papers). While China (73.43 and 2.69), France (57.60 and 2.11,) and Germany (50.13 and 1.84) registered the highest citation impact by CPP and RCI. Harvard Medical School, USA, Huazhong University of S&T, China, and Tongji Medical College, China, lead with most of the publications (4,4, 41, and 39 papers). In contrast, Tongji Medical College, China (198.31 and 7.26), Huazhong University of S&T, China (198.15 and 7.26), and University College Hospital NHS Foundation Trust, U.K (129.23 and 4.73) lead in citation impact. J.H. Levy, T. Iba and M. Levy contributed to most of the publications (27, 25, and 20), while N. Tag (804.75 and 29.48), A. Tripodi (157.83 and 5.78), and J. Thachil (154.17 and 5.65) registered the highest citation impact by CPP and RCI. Journal of Thrombosis and Haemostasis, Journal of Thrombosis and Thrombolysis, and Thrombosis Research were the most productive ones (with 55, 51, and 46 publications). At the same time, Medical Hypotheses (1222), Blood (355.71) and Thrombosis Journal (233.86) were the most impactful journals. The top 9 keywords were Covid-19 (1681 occurrences), Blood Clotting (952), D Dimer (652), C (628), Coagulopathy (628), Thrombosis (584), blood (506), Anticoagulants (499), Blood Coagulation (426) and Fibrinogen (376). Conclusions. Global research on Covid-19 associated coagulopathy since the pandemic was considered for scientometric assessment for the first time, combining the productivity and citation measures to present an overall picture of the literature in this area. Such an analysis will provide scholars and policy-makers with a meaningful reference for further exploration of topical issues and research trends in the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".