Primary prevention of stroke: blood pressure, lipids, and heart failure
Bibliographic record
Abstract
Stroke contributes significantly to morbidity, mortality, and disability worldwide. Despite the successes accomplished in the acute treatment and rehabilitation of stroke, the global burden of this disease can only be tackled with co-ordinated approaches for primary prevention. Stroke is a heterogeneous disease and the contribution of individual risk factors to its occurrence estimated by population attributable risk differs from coronary heart disease. Here, we review evidence to demonstrate the prominent role of elevated blood pressure (BP) and heart disease on risk of stroke, while the influence of lipids on stroke is less clear; we also demonstrate that stroke is an important complication of heart failure. Current approaches to primary preventive action emphasize the need to target the absolute risk of cardiovascular diseases rather than individual risk factors. Lifestyle interventions serve as a basis for primary prevention of cardiovascular diseases. It is estimated that 70% of strokes are potentially preventable by lifestyle modification but prospective evidence is needed to support these hypotheses derived from epidemiological studies. Different strategies for drug interventions in primary prevention are discussed, including the polypill strategy. Additional measures are needed for the primary prevention of stroke which focus on BP, chronic heart failure, and possibly lipids.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".