Knowledge mapping of B cell and atherosclerosis over the past 20 years: A bibliometric analysis
Bibliographic record
Abstract
Atherosclerosis (AS) is the main underlying cause of cardiovascular disease, and B cells are considered a key immune cell type to regulate AS. So far, there is no bibliometric study on B cell and AS. This study aims to comprehensively analyze the scientific output about B cell and AS, summarize the literature characteristics, explore research hotspots, and point out emerging trends. We searched the literature from 2003 to 2022 from the Web of Science Core Collection (WoSCC) database. CiteSpace, VOSviewer, and the R package "Bibliometrix" were used for literature analysis and visualization. A total of 1,062 articles and reviews were identified. The number of annual publications generally showed an upward trend. The United States and China were the most productive countries. Medical University of Vienna was the most productive research institution, and Binder Christoph J. was the most productive author, who was also from Medical University of Vienna. "Arteriosclerosis Thrombosis and Vascular Biology" was the most published journal and the most frequently cited journal. The most cited reference was written by Caligiuri G (2002) in "Journal of Clinical Investigation." The most frequent keywords were "inflammation," "macrophages," "cardiovascular disease," "T cells," "apoptosis," "immunity," "cytokines," "lymphocytes," etc. The trend topics were mainly focused on "immune infiltration," "immunoglobulins," and "biomarkers." The complex role of B cell subtypes and a variety of B cell mediators is the main research direction at present. In-depth analysis of B cell-specific targets can provide new ideas and methods for the prevention and treatment of AS.
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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.166 | 0.208 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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, 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".