Global Landscape of Cardiovascular Research: A Continental Bibliometric Analysis Standardized by Population and Physician Density
Bibliographic record
Abstract
Abstract Background Cardiovascular diseases (CVDs) remain the leading cause of global mortality. Despite rising research output, geographic disparities persist in the production and dissemination of scientific knowledge. Objectives To evaluate cardiovascular research productivity across world regions by normalizing publication output to population size and physician density, and to quantify disparities using Z-scores. Methods A comprehensive bibliometric analysis was conducted using Medline/PubMed to identify CVD-related publications across all 193 UN-recognized countries. Data were standardized using 2025 population estimates and WHO-reported physician density. Two standardized metrics were computed : publications per million population and publications per physician density. Z-scores were calculated to assess research productivity relative to global means. Results A total of 474,599 CVD publications were identified. Europe led in absolute volume (34.2%), followed by the Americas (30.7%) and Asia (25.8%). When adjusted per population density, Oceania (428.6), North America (336.7), and Western Europe (268.1) ranked highest. In contrast, Asia (15,917.3) and Sub-Saharan Africa (10,209.2) demonstrated the highest efficiency when adjusted for physician density. Z-score analysis revealed that Oceania (Z = +1.80), North America (Z = +1.25), and Western Europe (Z = +0.83) outperformed other regions in per capita productivity. However, when adjusted for physician density, Asia (Z = +2.18) and Sub-Saharan Africa (Z = +1.17) emerged as unexpectedly efficient despite limited workforce capacity. At the national level, Canada (Z = +2.44) and Japan (Z = +1.37) excelled per capita, while the United States (Z = +2.72) and Japan (Z = +1.83) led in physician-adjusted output. Conclusion Major geographic disparities exist in cardiovascular research output. Standardization by population and physician densities reveals high-efficiency zones in lowerresource settings, challenging assumptions about global scientific productivity. Strategic investment in underrepresented regions is essential to foster equitable knowledge generation and strengthen global cardiovascular health.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.072 | 0.128 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".