Polypill and Riskometer to Prevent Stroke and Cognitive Impairment in Primary Health Care (PROMOTE) Randomized Clinical Trial: Rationale and Design
Bibliographic record
Abstract
INTRODUCTION: Stroke and dementia have common modifiable risk factors. Current prevention strategies primarily focus on high-risk populations, leaving a gap in addressing the broader population. We report the protocol for a randomized controlled trial (RCT) that aims to evaluate the feasibility, tolerability, and effectiveness of a polypill (valsartan 80 mg, amlodipine 5 mg, and rosuvastatin 10 mg), with and without use of the Stroke Riskometer app, on systolic blood pressure (SBP) and other cardiovascular disease (CVD) risk factors at 9 months after randomization in a population of low to borderline CVD risk. METHODS: A prospective, pragmatic, multicentre, factorial, phase III, placebo-controlled, cluster RCT in low to moderate CVD risk (10-year risk <20%) individuals aged 50-75 years with no prior history of hypertension, diabetes mellitus, stroke, or other CVD, with a SBP of 121-139 mm Hg and at least one lifestyle-related CVD risk factor. Primary care units in Porto Alegre, Brazil, were centrally randomized to either use of the Stroke Riskometer app or standard care for lifestyle modification. All eligible individuals underwent a 28-day open run-in phase using the active medication. Participants who tolerated and had high adherence were randomized to either polypill or placebo, using a minimization process according to age, sex, SBP, cholesterol, and education level. The dual primary outcomes were change in SBP and Life's Simple 7 (LS7) score at 9 months post-randomization. A sample of 354 participants was estimated to provide 80% statistical power (two-sided α = 0.05, β = 0.20) for 6 clusters with intra-cluster correlation of 0.01 to detect a clinically significant 2.5-mm Hg (SD ± 8) difference in SBP change and 0.65 points (SD ± 1.61) difference in the LS7 score at 9 months post-randomization between the polypill/Stroke Riskometer group and placebo/usual care group, assuming 10% lost to follow-up. All analyses were conducted according to the intention-to-treat principle. Regression analysis models (ANCOVA) assessed the differences among the four groups concerning changes in SBP, cholesterol levels, cognitive function, and behavioural risk factors over time. CONCLUSION: The findings will provide critical information to allow the development of primary stroke and CVD prevention strategies in low to borderline CVD risk adults.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".