Stroke and Cardiovascular Diseases: The Need for a Global Approach for Prevention and Drug Development
Bibliographic record
Abstract
BACKGROUND: Research into the prevention and treatment of stroke and cardiovascular disease has focused primarily on the needs of high-income countries (HIC). However, the majority of all stroke and cardiovascular deaths occurs in low- and middle-income countries (LMIC), with further rises in these countries predicted. SUMMARY OF REVIEW: In HIC, proven strategies for the treatment of stroke and cardiovascular disease are well established and cost-effective. Developing strategies to include LMIC is therefore crucial to curb the global epidemic of stroke and cardiovascular disease. For example, pharmaceutical companies are being encouraged to make certain drugs more affordable in low- and middle-income companies, and the same principle could be applied to drugs for the prevention of stroke. Furthermore, centers from LMIC are now often included in clinical trials, resulting in trials that are more globally relevant and affordable and that enhance the participation of healthcare professionals from a broad range of countries. CONCLUSIONS: More cost-effective drug development processes and affordable prices, while protecting intellectual property rights, will prevent the ever-increasing burden of stroke becoming unmanageable in LMIC.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".