Epidemiology of Young Stroke in the Ludhiana Population-Based Stroke Registry
Bibliographic record
Abstract
Objective: The objective of the study was to determine incidence, risk factors, and short-term outcomes of young stroke in Ludhiana city, Northwest India. Methods: Data were collected on first-ever stroke in patients of age ≥18 years, from hospitals, diagnostic imaging centers, general practitioners, and municipal corporation during March 2011–March 2013 in Ludhiana city, using the World Health Organization Stepwise Approach to Surveillance (WHO STEPS). Outcome was documented using the modified Rankin Scale at 28 days. Results: Of 2948 patients, 700 (24%) were in the age group 18–49 years. Annual incidence in this age group was 46/100,000 person-years (95% confidence interval [CI], 41–51/100,000). Hypertension (84%), diabetes mellitus (48%), and atrial fibrillation (AF) (12%) were found more common in >49 years age group, as compared with 18–49 years age group. Drug abuse (8.7% vs. 6% in age >49 years; P = 0.04) and tobacco intake (8.7% vs. 5.6% in age >49 years; P = 0.02) was more common in young people, that is, 18–49 years age group in comparison to older patients, >49 years age group. Recovery was better in younger subjects (60% vs. 46% in age >49 years P < 0.001). In a multivariable analysis, younger people were more often literate (odds ratio [OR] 2.52; 95% CI, 1.68–3.77; P < 0.001), employed (OR 3.92; 95% CI, 2.20–5.21; P < 0.001), and 374 (60%) had good clinical outcome, modified Rankin Scale <2 at 28 days follow-up as compared with 938 (46%) older patients (OR 1.52; 95% CI, 1.15–2.00; P = 0.003). Conclusion: Hypertension, diabetes mellitus, drug addiction, and tobacco intake were significantly associated with young stroke. Outcome was also better in younger people.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 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".