Educational and Socioeconomic Correlates of Stroke Risk Behaviors: Findings from the SPRINT INDIA Trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Secondary Prevention by Structured Semi-Interactive Stroke Prevention Package in India (SPRINT INDIA) trial was a randomized control trial that enrolled 4298 stroke patients and administered educational interventions at 31 centers across India, with the aim to reduce recurrent stroke through increased stroke knowledge. This SPRINT INDIA trial post hoc study aims to investigate the incidence of recurrent stroke, high-risk transient ischemic attack (TIA), acute coronary syndrome (ACS), death, and lifestyle behavioral factors at 1 year. In addition, it examines the relationship between patients' baseline characteristics and education levels, risk factors, and outcomes and performs subgroup analysis within the intervention and control groups. METHODS: Participants were randomly assigned (1:1) to either intervention or control group through computer-based randomization on web. Intervention included stroke prevention Short Message Service messages, short-duration videos, and printed workbooks. Baseline assessments captured demographic and educational data, classifying patients into three categories: no schooling, less than high school, and high school or above. Primary outcome was a composite of recurrent stroke, high-risk TIA, ACS, and mortality at 1 year. Chi-square tests and analysis of variance were used to evaluate educational disparities across various variables. RESULTS: The intervention did not reduce primary outcomes at 1 year among patients with different educational levels. Higher educational group was associated with enhanced medication adherence (94.3% vs 85.4%; P < 0.001), increased physical activity (5497.91 ± 4117.7 vs 6169.91±4828.8; P < 0.001), lower triglyceride levels, and decreased engagement in behavioral risk factors like alcohol intake (5.1% vs 6.8%; P = 0.013) and tobacco use (smoked and chewed) (4% vs 7.9%; P < 0.001 and 5.8% vs 11.6%; P = 0.020). CONCLUSIONS: Personalized secondary stroke prevention, tailored to educational levels, is crucial for effective stroke management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".