MyRisk_Stroke Calculator: A Personalized Stroke Risk Assessment Tool for the General Population
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There is a variety of stroke risk factors, and engaging individuals in reducing their own personal risk is hugely relevant and could be an optimal dissemination strategy. The aim of the present study was to estimate the stroke risk for specific combinations of health- and lifestyle-related factors, and to develop a personalized stroke-risk assessment tool for health professionals and the general population (called the MyRisk_Stroke Calculator). METHODS: This population-based, longitudinal study followed a historical cohort formed from the 1992 or 1998 Santé Québec Health Surveys with information for linkage to health administrative databases. Stroke risk factors were ascertained at the time of survey, and stroke was determined from hospitalizations and death records. Cox proportional hazards models were used, modeling time to stroke in relationship to all variables. RESULTS: A total of 358 strokes occurred among a cohort of 17805 persons (men=8181) who were followed for approximately 11 years (i.e., -200000 person-years). The following regression parameters were used to produce 10-year stroke-risk estimates and assign risk points: for age (1 point/year after age 20 years), male sex (3 points), low education (4 points), renal disease (8 points), diabetes (7 points), congestive heart failure (5 points), peripheral arterial disease (2 points), high blood pressure (2 points), ischemic heart disease (1 point), smoking (8 points), >7 alcoholic drinks per week (3 points), low physical activity (2 points), and indicators of anger (4 points), depression (4 points), and anxiety (3 points). According to MyRisk_Stroke Calculator, a person with <50, 75, and 90 risk points has a 10-year stroke risk of <3%, 28%, and >75%, respectively. CONCLUSIONS: The MyRisk_Stroke Calculator is a simple method of disseminating information to the general population about their stroke risk.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.016 | 0.005 |
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".