SPRINT INDIA: Regional Variations in Primary and Secondary Stroke Outcomes Based on Baseline Characteristics in North and South Indian Sites
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Regional differences in stroke prevalence and outcomes in India, driven by demographic and risk factors, are crucial for guiding effective prevention and management strategies. This subanalysis of Secondary prevention with a structured semi-interactive stroke prevention package in INDIA (SPRINT INDIA) randomized controlled trial compared the demographics, risk factors, and clinical outcomes of stroke patients from North and South India to identify regional differences and inform targeted interventions for stroke prevention. METHODS: The study analyzed data of 4298 participants from 31 stroke centers across India, focusing on demographics, stroke types, and risk factors. In this study, Mumbai, located at 19.07°N in western India, serves as the dividing line between North and South India. One-year follow-up data from 3038 patients were utilized to examine regional disparities between North and South India. RESULTS: South Indian stroke patients were predominantly rural (60.1%) and less educated (58.2%), while North Indian patients were mostly urban (64.2%). South Indian patients had higher incidence of ischemic stroke (91.1% vs. 73.5%, P = 0.001) and higher rates of large artery atherosclerosis (33.6% vs. 19.7%, P = 0.001), hypertension, type 2 diabetes, smoking, and alcohol consumption, but better medication adherence. In contrast, North Indian patients had higher high-density lipoprotein, drug use, and tobacco use. At 1-year follow-up, North Indian patients had more high-risk transient ischemic attacks and poorer lifestyle-related outcomes, despite South Indians having higher systolic blood pressure and fasting glucose levels. CONCLUSION: Region-specific strategies are crucial. Block randomization may help. South India needs better lifestyle modification programs, while North India requires improved health education and medication adherence strategies.Trial registration: CTRI/2017/09/009600.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".