Disparities in Cardiovascular Research Output and Disease Outcomes among High-, Middle- and Low-Income Countries – An Analysis of Global Cardiovascular Publications over the Last Decade (2008–2017)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of death and disability worldwide. Health research is crucial to managing disease burden. Previous work has highlighted marked discrepancies in research output and disease burden between high-income countries (HICs) and low- and lower-middle-income countries (LI-LMICs) and there is little data to understand whether this gap has bridged in recent years. We conducted a global, country level bibliometric analysis of CVD publications with respect to trends in disease burden and county development indicators. Methods: A search filter with a precision and recall of 0.92 and 0.91 respectively was developed to extract cardiovascular publications from the Web of Science (WOS) for the years 2008-2017. Data for disease burden and country development indicators were extracted from the Global Burden of Disease and the World Bank database respectively. Results: Our search revealed 847,708 CVD publications for the period 2008-17, with a 43.4% increase over the decade. HICs contributed 81.1% of the global CVD research output and accounted for 8.1% and 8.5% of global CVD DALY losses deaths respectively. LI-LMICs contributed 2.8% of the total output and accounted for 59.5% and 57.1% global CVD DALY losses and death rates. Conclusions: A glaring disparity in research output and disease burden persists. While LI-LMICs contribute to the majority of DALYs and mortality from CVD globally, their contribution to research output remains the lowest. These data call on national health budgets and international funding support to allocate funds to strengthen research capacity and translational research to impact CVD burden in LI-LMICs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it