COVID-19 from a rheumatology perspective: bibliometric and altmetric analysis
Bibliographic record
Abstract
The Coronavirus disease 2019 (COVID-19) outbreak turned out the greatest pandemic for decades. It challenged enormously the global health system, forcing it to adjust to the new realities. We aimed to analyze articles covering COVID-19 papers in the rheumatological field and outline emerging topics raising within this frame. We applied the bibliometric database Scopus for our literature search and conducted it on the 5th of June using the following keywords: "rheumatic" OR "rheumatology" OR "rheumatoid arthritis" OR "systemic lupus erythematosus" OR "myositis" OR "systemic sclerosis" OR "vasculitis" OR "arthritis" OR "ankylosing spondylitis" AND "COVID-19". We analyzed all selected articles according to various aspects: type of document, authorship, journal, citations score, rheumatology field, country of origin, language, and keywords. With the help of the software tool VOSviewer version 1.6.15, we have built the visualizing network of authors and keywords co-occurrence. The measurement of the social impact of articles was made using Altmetric data. This study included 1430 retrieved articles with open access mostly. The top five journals in this field were Annals of the Rheumatic Diseases (n = 65), Rheumatology International (n = 51), Clinical Rheumatology (n = 50), Lancet Rheumatology (n = 50), and Frontiers In Immunology (n = 33). Most studies originate from countries with a high incidence of COVID-19 among the general population (the USA-387; Italy-268; UK-184; France-114; Germany-110; India-98 and Spain-96, China-94, Canada-73 Turkey-66). Original Articles (42.1%) were the most common articles' type, following by Letters (24.4%), Reviews (21.7%), Notes (6%), Editorials (4.8%), Erratum (1%). According to the citations scores, articles dedicated to the clinical course of COVID-19 in patients with rheumatic diseases were of the highest importance for the scientific rheumatologic community. Rheumatoid arthritis (n = 527), systemic lupus erythematosus (n = 393), vasculitis (n = 267), myositis (n = 71), systemic sclerosis (n = 68), and psoriatic arthritis (n = 68) were the most widely discussed rheumatic diseases in the view of COVID-19. The analysis of Altmetric and citations scores revealed a moderate correlation between them. This article provides a comprehensive bibliometric and altmetric analysis of COVID-19 related articles in the rheumatology field and summarizes data about features of rheumatology service in the time of the pandemic.
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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 | 0.001 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.072 | 0.061 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".