Does national expenditure on research and development influence stroke outcomes?
Bibliographic record
Abstract
Background Expenditure on research and development is a macroeconomic indicator representative of national investment. International organizations use this indicator to compare international research and development activities. Aim We investigated whether differences in expenditures on research and development at the country level may influence the incidence of stroke and stroke mortality. Methods We compared stroke metrics with absolute amount of gross domestic expenditure on R&D (GERD) per-capita adjusted for purchasing power parity (aGERD) and relative amount of GERD as percent of gross domestic product (rGERD). Sources included official data from the UNESCO, the World Health Organization, the World Bank, and population-based studies. We used correlation analysis and multivariable linear regression modeling. Results Overall, data on stroke mortality rate and GERD were available from 66 countries for two periods (2002 and 2008). Age-standardized stroke mortality rate was associated with aGERD (r = -0.708 in 2002 and r = -0.730 in 2008) or rGERD (r = -0.545 in 2002 and r = -0.657 in 2008) (all p < 0.001). Multivariable analysis showed a lower aGERD and rGERD were independently and inversely associated with higher stroke mortality (all p < 0.05). The estimated prevalence of hypertension, diabetes, or obesity was higher in countries with lower aGERD. The analysis of 27 population-based studies showed consistent inverse associations between aGERD or rGERD and incident risk of stroke and 30-day case fatality. Conclusions There is higher stroke mortality among countries with lower expenditures in research and development. While this study does not prove causality, it suggests a potential area to focus efforts to improve global stroke outcomes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".