A bibliometric analysis of income and cardiovascular disease
Bibliographic record
Abstract
BACKGROUND: Income is 1 of the socio-economic indicators and could directly influence the health outcomes of cardiovascular disease (CVD). The relationship between income and CVD has attracted more and more scholars' attention in the past 20 years. METHODS: To study the current research dynamics of this field, a bibliometric analysis was conducted to evaluate the publications from 1990 to 2018 based on the Science Citation Index Expanded database. By using the Derwent Date Analyzer software, the following aspects were explored: RESULTS:: The USA ranked first in this field, followed by UK and Canada in terms of number of publications. As for institutions, Harvard University took the leading place in the number of publications, as well as the h-index. Plos One had the most publications and "health" was the most frequent used keyword. The leading research area was "public environmental occupational health". CONCLUSIONS: In conclusion, the elderly, the children and the puerpera were the main study population in this field and "disease prevention" was the main study direction. The most concerned health issues in this field were "obesity" and "diet". There might be a lack of articles that explore the associations between income and CVD with a global perspective. Articles on this content are urgently warranted.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.103 | 0.125 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".