Global landscape of COVID-19 research: a visualization analysis of randomized clinical trials
Bibliographic record
Abstract
The emergence of COVID-19 in 2019 has resulted in a significant global health crisis. Consequently, extensive research was published to understand and mitigate the disease. In particular, randomized controlled trials (RCTs) have been considered the benchmark for assessing the efficacy and safety of interventions. Hence, the present study strives to present a comprehensive overview of the global research landscape pertaining to RCTs and COVID-19. A bibliometric analysis was performed using the Scopus database. The search parameters included articles published from 2020 to 2022 using keywords specifically related to COVID-19 and RCTs. The data were analyzed using various bibliometric indicators. The volume of publications, contributions of countries and institutions, funding agencies, active journals, citation analysis, co-occurrence analysis, and future research direction analysis were specifically analyzed. A total of 223,480 research articles concerning COVID-19 were published, with 3,727 of them related to RCTs and COVID-19. The ten most productive countries collectively produced 75.8% of the documents, with the United States leading the way by contributing 31.77%, followed by the UK with 14.03% (n = 523), China with 12.96% (n = 483) and Canada with 7.16% (n = 267). Trials (n = 173, 4.64%), BMJ Open (n = 81, 2.17%), PLOS One (n = 73, 1.96%) and JAMA Network Open (n = 53, 1.42%) were the most active journals in publishing articles related to COVID-19 RCTs. The co-occurrence analysis identified four clusters of research areas: the safety and effectiveness of COVID-19 vaccines, mental health strategies to cope with the impact of the pandemic, the use of monoclonal antibodies to treat patients with COVID-19, and systematic reviews and meta-analyses of COVID-19 research. This paper offers a detailed examination of the global research environment pertaining to RCTs and their use in the context of the COVID-19 pandemic. The comprehensive body of research findings was found to have been generated by the collaborative efforts of multiple countries, institutions, and funding organizations. The predominant research areas encompassed COVID-19 vaccines, strategies for mental health, monoclonal antibodies, and systematic reviews. This information has the potential to aid researchers, policymakers, and funders in discerning areas of weakness and establishing areas of priority.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.152 | 0.528 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.072 | 0.088 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".