The Stroke Riskometer™ in the Outpatient Clinic as an Educational Campaign for Acute Ischemic Stroke
Bibliographic record
Abstract
Introduction: Stroke is highly prevalent worldwide; however, associated symptoms and risk factors are unknown in the general population. Our aim was to describe the knowledge of early signs of stroke and its association with the risk of stroke at 5 and 10 years according to the “Stroke Riskometer™” Subjects and Methods: This was an observational, descriptive, cross-sectional study, including adults in the Neurology outpatient clinic of the University Hospital “Dr. José Eleuterio González”. Vital signs were recorded, anthropometric and the “Stroke Riskometer™” measurements were collected, and the risks at 5 and 10 yearswere calculated. Patients were questioned about the early signs of stroke (with emphasis on the acronym FAST: F = Face uneven, A = Arm hanging down, S = Speech slurred, T = Time is vital [CAMALEÓN in Spanish]). Spearman’s evaluation was used to measure the association between risk and knowledge of signs. Results: A total of 300 participants were included; 208 (69.3%) were women and the mean age was 54.5 (±14.0) years. The most prevalent risk factors for stroke were sedentary lifestyle (46.3%), high blood pressure (40.0%), and diabetes (31.0%). The population median risk at 5 years was 3.6% (interquartile range (IQR) 1.9–7.0) and at 10 years 6.3% (IQR 3.1–14.0). Of all participants, 31.2% were aware of at least one early sign of stroke. No significant correlation was found between awareness of early signs and risk at 5 or 10 years (r = 0.039, p = 0.5; r = –0.05, p = 0.380, respectively). Conclusions: Knowledge of the signs of stroke is low but remains an ongoing goal for educational campaigns in Mexico. A large-scale national and long-lasting campaign is necessary, given the high risk of stroke in the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".