Epidemiological and Clinical Characteristics of Acute Stroke in a Multi-Ethnic South Asian Population
Bibliographic record
Abstract
Objective: Stroke is one of the leading causes of death and disability worldwide. Compared to developed countries, the prognosis of stroke is less favourable in developing countries. The objective of this study is to identify inter-ethnic variation in risk profiles and stroke outcomes amongst Bangladeshi, Indian, Nepalese, Pakistani, and Sri Lankan expatriates living in Qatar. Methods: Data from the Qatar Stroke Registry were retrospectively analyzed from April 2014 to June 2025. A total of 8825 patients were included. The chi-square test was used to analyze sociodemographic variables, while the Kruskal–Wallis test was used to analyze continuous variables. Post hoc analysis was performed. Multivariate logistic regression and multivariate multiple regression were used to identify the predictors associated with poor clinical outcomes and mortality at 90 days. Results: Ischemic stroke was the predominant stroke type in all groups, with Nepalese patients presenting with stroke at a younger age, whilst Pakistanis tended to be older (p < 0.001). In terms of stroke outcomes, Nepalese patients had the highest proportion of a poor functional outcome at 90 days as well as NIHSS at discharge (p < 0.05). However, Bangladeshis had the highest proportion of mortality at 90 days compared to the other cohorts. Multivariable logistic regression revealed that undiagnosed dyslipidemia, Nepalese ethnicity, and moderate and severe NIHSS admission scores were independent predictors of a poor functional outcome at 90 days, whilst male sex and prior antidiabetic therapy were protective factors (p < 0.001). In terms of mortality at 90 days, only a severe NIHSS admission score (>10) was a significant predictor (p < 0.001). A severe NIHSS admission score was also the only predictive factor of mortality and poor functional outcome at 90 days (p < 0.05). Conclusions: There was a significant variation in stroke presentation and outcomes among South Asian subpopulations in Qatar, suggesting the importance of tailored public health strategies as a uniform approach to stroke care is insufficient for this diverse population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".