A visualization analysis of hotspots and frontiers of cardiovascular diseases with frailty
Bibliographic record
Abstract
Cardiovascular diseases (CVD) and frailty are common health problems among the elderly. This research aims to investigate the hotspots and frontiers of the field of CVD with frailty. Data of publications between 2000 and 2021 were collected from the Web of Science Core Collection (WoSCC) and CiteSpace was used for analyzing the hotspots and frontiers of cardiovascular diseases with frailty research from high-impact countries/regions, institutions, authors, cited references, cited journals, high-frequency keywords, and burst keywords. The results showed that the USA, England, and Canada were the leading countries/regions in research on CVD with frailty. Other countries/regions and regions lagged behind these developed countries/regions. There is a need to establish cooperation between developed countries/regions and developing countries/regions. Research hotspots focused on frailty in the elderly with CVD, exercise intervention, assessment for CVD patients with frailty, quality of life, and common diseases related to CVD with frailty. The frontier fields include care and intervention of CVD patients with frailty, social frailty, and validation of CVD with frailty.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.036 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".