The population approach to stroke prevention: a Canadian perspective.
Bibliographic record
Abstract
OBJECTIVES: To contrast the population and clinical approaches to stroke prevention and to review Canadian data relevant to estimating disease burden, assessing risk factors, designing preventive strategies and organizing health services. METHOD: A narrative review of the published literature and statistical data accessible through the Internet. MAIN FINDINGS: Unlike the clinical approach, which emphasizes individual patients at high risk diagnosed and treated intensively, usually by medical or surgical means (or both), a population approach focuses on the entire population and bases interventions on behavioural and environmental changes. Stroke offers a particularly promising target for prevention. It represents a leading cause of serious disability, death and reduced quality of life. The aging of our population threatens to increase the already considerable burden. Stroke shares several risk factors with other chronic diseases, especially ischemic heart disease. These risk factors vary in their impact on Canadians (population attributable risk proportion), which is a function of their prevalence and strength of association with the occurrence of stroke. Although effective preventive measures are available for people at high risk, they are not being applied systematically among potential beneficiaries. Small reductions in the exposure to risk factors in the entire population offer an alternative, where even modest success may translate into major gain. CONCLUSIONS: The clinical and population approaches to stroke prevention are complementary. Existing national strategies directed at promotion of healthy life-styles (especially physical activity) and hypertension control, when fully implemented, will reduce the frequency, severity and impact of stroke on Canadian society.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".