Global research trends and hotspots in aspirin studies (2014–2024): a bibliometric perspective
Bibliographic record
Abstract
Background: Aspirin, as one of the most important drugs in medical history, has been continuously explored for over 3,000 years. This study employs bibliometric analysis to examine the research hotspots and trends on aspirin over the past decade. Methods and results: This study retrieved articles and reviews on aspirin from the Web of Science database, covering the period from 2014 to 2024. R software and CiteSpace were employed for visual analysis, revealing trends in publication volume, collaborations, core journals, and keywords distributions. In the past decade, a total of 19,504 papers authored by 88,600 researchers were published, citing 460,704 references. The U.S., China, and Italy lead in publications, with Canada and Australia showing strong collaboration. The authors with the highest contributions include BHATT DL, STEG PG, and WANG YJ. Research hotspots and trends include the following three points: the development of expert recommendations for the use of aspirin in primary cardiovascular prevention into personalized and shared decision-making between doctors and patients; the ongoing need for more evidence regarding the effects of aspirin on different tumors; and the sustained focus on aspirin-related respiratory diseases in future research. Conclusion: Aspirin, a classic drug, continues to have a substantial number of publications, underscoring its lasting impact. The United States, China, and Italy play a leading role in this field. However, there is still a long way to go, and research that is more targeted and beneficial for different refined populations may be a future trend.
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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: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.127 | 0.220 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".