Exploring the link trend in the field of coronaviruses and cognitive impairment: A bibliometric analysis based on bibliometrix
Bibliographic record
Abstract
BACKGROUND: Coronaviruses (CoVs) significantly impact human health, targeting the respiratory and nervous systems and causing long-term complications such as cognitive impairment. While the cognitive effects of CoVs, including severe acute respiratory syndrome CoV, are well-documented, a comprehensive analysis of the evolving research landscape remains unexplored. METHODS: We performed a bibliometric analysis of CoV-related cognitive research from 1998 to 2025 using data from the Web of Science Core Collection. Bibliometrix software was employed to examine publication trends, geographical contributions, institutional output, author collaborations, and research hotspots. RESULTS: Among 4,076 publications analyzed, a dramatic rise in research output was observed post-2020, correlating with the COVID-19 pandemic. The United States led in publication count (24.63%) and citations, followed by Italy and China. The University of Toronto is ranked as the most prolific institution. The most highly cited articles are from Alzheimer's and Dementia, The Lancet Infectious Diseases, and eClinicalMedicine. Cao Bing, Mazza, Mario Gennaro, and Wang Yi had the most influence on CoV impact on cognitive impairment. Keyword analysis revealed emerging research themes such as "depression," "anxiety," and "health," reflecting the psychological and cognitive effects of the pandemic. Highly cited articles identified neuroinflammatory and neuroimmune pathways, emphasizing the role of viral invasion in cognitive dysfunction. CONCLUSION: The COVID-19 pandemic has driven a surge in studies linking CoV infections to cognitive impairment. This research highlights mechanisms such as blood-brain barrier disruption, neuronal damage, and altered cerebral glucose metabolism. Future studies should focus on standardized diagnostic criteria and therapeutic strategies to mitigate long-term cognitive sequelae.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.141 | 0.285 |
| Science and technology studies | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".