Socioeconomic Status and Long-Term Stroke Mortality, Recurrence and Disability in Iran: The Mashhad Stroke Incidence Study
Bibliographic record
Abstract
BACKGROUND: Little is known about the association between socioeconomic status and long-term stroke outcomes, particularly in low- and middle-income countries. METHODS: Patients were recruited from the Mashhad Stroke Incidence Study in Iran. We identified different socioeconomic variables including the level of education, occupation, household size, and family income. Residential location according to patient's neighbourhood was classified into less privileged area (LPA), middle privileged area and high privileged area (HPA). Using Cox regression, competing risk analysis and logistic regression models, we determined the association between socioeconomic status and 1- and 5-year stroke outcomes. Generalized linear model was used for adjusting associated variables for stroke severity. RESULTS: Six hundred twenty-four patients with first-ever stroke were recruited in this study. Unemployment prior to stroke was associated with an increased risk of 1- and 5-year post-stroke mortality (1 year: adjusted hazard ratio [aHR] 3.3; 95% CI 1.6-7.06: p = 0.001; 5 years: aHR 2.1; 95% CI 1.2-3.6: p = 0.007). The 5-year mortality rate was higher in less educated patients (<12 years) as compared to those with at least 12 years of schooling (aHR 1.84; 95% CI 1.05-3.23: p = 0.03). Patients living in LPA compared to those living in HPAs experienced a more severe stroke at admission (aB 3.84; 95% CI 0.97-6.71, p = 0.009) and disabling stroke at 1 year follow-up (OR 6.1; 95% CI 1.3-28.4; p = 0.02). CONCLUSION: A comprehensive stroke strategy should also address socioeconomic disadvantages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".