Primordial and Primary Prevention of Ischemic Stroke in Saudi Arabia: A Combination Approach and Evolving Concepts
Bibliographic record
Abstract
Ischemic stroke is a considerable public health hazard and a significant cause of disability and mortality in Saudi Arabia. Primary prevention strategies in the country are currently limited. With the health sector transformation program that depends on the principles of value-based care and applying the new model of care in disease prevention, aggressive and serious steps for primary stroke prevention are expected to be implemented. This article reviews primordial and primary prevention of ischemic stroke in Saudi Arabia and suggests a combination approach and framework for implementation. We provide a pragmatic solution to implement primordial and primary stroke prevention in Saudi Arabia and specify the roles of the government, health professionals, policymakers, and the entire population. Currently, there are several key priorities for primordial and primary stroke prevention in Saudi Arabia that should target people at different levels of risk. These include an emphasis on a comprehensive approach that includes both individual and population-based strategies and establishing partnerships across health-care providers to share responsibility for developing and implementing both strategies. This is an urgent call for action to initiate different strategies suggested by experts for primary stroke prevention in Saudi Arabia.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".