Visual Analysis of Research Trends and Hotspots in Traditional Chinese Medicine-Based Treatment of Chronic Heart Failure
Bibliographic record
Abstract
BACKGROUND:Chronic heart failure (CHF) has gradually become the most important chronic cardiovascular disease of the 21st century. Although traditional Chinese medicine (TCM)-based treatments can effectively improve the quality of life of CHF patients, there is no bibliometric systematic analysis to prove it. Therefore, this study aimed to review the literature, identify the research hotspots and frontiers of TCM in the treatment of CHF, and provide a reference for further research. MATERIAL AND METHODS:Studies on the treatment of CHF by TCM were downloaded from 3 databases. We used VOSviewer1.6.18.0, Microsoft Excel 2019, and CiteSpace 6.1.R2 for visualization and bibliometric research. RESULTS:From 1990 to 2022, a total of 1959 articles published by 11 013 researchers from 3242 institutions in 82 countries/territories were included, with an overall increase in the number of articles published, especially in 2021. The United States and China were the main countries. The University of Groningen, University of Glasgow, and University of Toronto were the leading research institutions, and half of the top 10 institutions were based in the United States. The most published academic was Gree C Fonarow, and the most cited author was McMurray JJV. The European Journal of Heart Failure and Circulation were the most published and cited journals, respectively. The research in this field is divided into 4 main categories. CONCLUSIONS:The current research focus in this field is mainly on the pathogenesis, clinical treatment, and multi-organ interaction of CHF.
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.117 | 0.105 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".