Long-term disability after stroke in Iran: Evidence from the Mashhad Stroke Incidence Study
Bibliographic record
Abstract
BACKGROUND: Accurate information about disability rate after stroke remains largely unclear in many countries. Population-based studies are necessary to estimate the rate and determinants of disability after stroke. METHODS: Patients were recruited from the Mashhad Stroke Incidence Study and followed for five years after their index event. Disability was measured using the modified Rankin scale and functional dependency was measured using the Barthel index. RESULTS: Among 684 patients registered in this study, 624 were first-ever strokes. In total, 69.0% (n = 409) of patients either died or remained disabled at five-year follow-up. Among the first-ever stroke survivors, 18.5% (n = 69) at one year and 15.9% (n = 31) at five years required major assistance in their daily activities. Patients with a history of stroke (before the study period) compared with first-ever strokes were more likely to be disabled at one year (modified Rankin scale>2 in 40.0% vs. 19.1%; P < 0.001). Advanced age, severity of stroke at the time of admission, diabetes mellitus, and educational level (<12 years) were independently associated with greater disability and functional dependency. CONCLUSION: We found that significant disability and functional dependency after stroke in Northeast Iran were largely attributable to the effects of stroke severity and prior dependency.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".