When COVID-19 meets diabetes: A bibliometric analysis
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) survivors are concerned about the likelihood of developing further diseases. This study examines the global trends in scientific research on diabetes associated with COVID-19 from several perspectives. Bibliometric analyses are used to undertake a scientific review of the literature. The Web of Science Core Collection (WoSCC) database was used to acquire bibliographic information on diabetes related to COVID-19 from Jan 2020 to Dec. 2023. The visual map was built via advanced CiteSpace 6.2.R6. 7,348 papers were found. Khunti Kamlesh and Rizzo-Manfredi are the most well-known high-yield authors in this area, and the top ten authors collaborate extensively. Most of these papers came from universities. Harvard Medical School has the most publications, followed by Wuhan University and Huazhong University of Science and Technology. China and the United States are the countries with the most publications. Angiotensin-converting enzymes, chronic disease, intensive care unit, viral infection, and gestational diabetes mellitus were scored 0-11, 2, 3, and 4, respectively. Zhou et al.'s work on this topic, which appeared in the prominent medical journal The Lancet, was cited 1,366 times, highlighting its importance. "clinical characteristics," "diabetes mellitus," "metabolic syndrome," and "angiotensin-converting enzyme" were used as keywords for reference co-citation and clustering data identify. Over the last four years, related investigations have focused primarily on observing clinical aspects. This report is important for developing treatment strategies, directing future research, and guiding clinical practice.
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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 | 0.060 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.011 |
| Bibliometrics | 0.112 | 0.158 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".