High prevalence of modifiable stroke risk factors identified in a pharmacy-based screening programme
Bibliographic record
Abstract
BACKGROUND: Population-based screening for atrial fibrillation (AF) is a promising public health strategy to prevent stroke. However, none of the published reports have evaluated comprehensive screening for additional stroke risk factors such as hypertension and diabetes in a pharmacy setting. METHODS: The Program for the Identification of 'Actionable' Atrial Fibrillation in the Pharmacy Setting (PIAAF-Pharmacy) screened individuals aged ≥65 years, attending community pharmacies in Canada, who were not receiving oral anticoagulation (OAC). Participants were screened for AF using a hand-held ECG device, had blood pressure (BP) measured, and diabetes risk estimated using the Canadian Diabetes Risk Assessment Questionnaire (CANRISK) questionnaire. 'Actionable' AF was defined as unrecognised or undertreated AF. A 6-week follow-up visit with the family physician was suggested for participants with 'actionable' AF and a scheduled 3-month visit occurred at an AF clinic. RESULTS: -VASc score ≥2. A BP>140/90 was found in 54.9% (616/1122) of participants and 44.4% (214/492) were found to be at high risk of diabetes. At 3 months, only 17% of participants were started on OAC, 50% had improved BP and 71% had confirmatory diabetes testing. CONCLUSIONS: Integrated stroke screening identifies a high prevalence of individuals who could benefit from stroke prevention therapies but must be coupled with a defined care pathway.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".