Characteristics, clinical practice patterns, and outcomes of strokes in India: INSPIRE—A multicentre prospective study
Bibliographic record
Abstract
BACKGROUND: India has a high burden of stroke, but there are limited data available on the characteristics of patients presenting with stroke in India. AIMS: We aimed to document the clinical characteristics, practice patterns, and outcomes of patients presenting with acute stroke to Indian hospitals. METHODS: A prospective registry study of patients admitted with acute clinical stroke was conducted in 62 centers across different regions in India between 2009 and 2013. RESULTS: Of the 10,329 patients included in the prescribed registry, 71.4% had ischemic stroke, 25.2% had intracerebral hemorrhage (ICH), and 3.4% had an undetermined stroke subtype. Mean age was 60 years (SD = 14) with 19.9% younger than 50 years; 65% were male. A severe stroke at admission (modified-Rankin score 4-5) was seen in 62%, with 38.4% of patients having severe disability at discharge or dying during hospitalization. Cumulative mortality was 25% at 6 months. Neuroimaging was completed in 98%, 76% received physiotherapy, 17% speech and language therapy (SLT), 7.6% occupational therapy (OT), with variability among sites; 3.7% of ischemic stroke patients received thrombolysis. Receipt of physiotherapy (odds ratio (OR) = 0.41, 95% confidence interval (CI): 0.33-0.52) and SLT (OR = 0.45, 95% CI: 0.32-0.65) was associated with lower mortality, while a history of atrial fibrillation (OR = 2.22, 95% CI: 1.37-3.58) and ICH (OR = 2.00, 95% CI: 1.66-2.40) were associated with higher mortality. CONCLUSION: In the INSPIRE (In Hospital Prospective Stroke Registry) study, one-in-five patients with acute stroke was under 50 years of age, and one-quarter of stroke was ICH. There was a low provision of thrombolysis and poor access to multidisciplinary rehabilitation highlighting how improvements are needed to reduce morbidity and mortality from stroke in India.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".