Temporal trends in stroke incidence in South Asian, Chinese and white patients: A population based analysis
Bibliographic record
Abstract
BACKGROUND: Little is known about potential ethnic differences in stroke incidence. We compared incidence and time trends of ischemic stroke and primary intracerebral hemorrhage in South Asian, Chinese and white persons in a population-based study. METHODS: Population based census and administrative data analysis in the provinces of Ontario and British Columbia, Canada using validated ICD 9/ICD 10 coding for acute ischemic and hemorrhagic stroke (1997-2010). RESULTS: There were 3290 South Asians, 4444 Chinese and 160944 white patients with acute ischemic stroke and 535 South Asian, 1376 Chinese and 21842 white patients with intracerebral hemorrhage. South Asians were younger than whites at onset of stroke (70 vs. 74 years for ischemic and 67 vs. 71 years for hemorrhagic stroke). Age and sex adjusted ischemic stroke incidence in 2010 was 43% lower in Chinese and 63% lower in South Asian than in White patients. Age and sex adjusted intracerebral hemorrhage incidence was 18% higher in Chinese patients, and 66% lower in South Asian relative to white patients. Stroke incidence declined in all ethnic groups (relative reduction 69% in South Asians, 25% in Chinese, and 34% in white patients for ischemic stroke and for intracerebral hemorrhage, 79% for South Asians, 51% for Chinese and 30% in white patients). CONCLUSION: Although stroke rates declined across all ethnic groups, these rates differed significantly by ethnicity. Further study is needed to understand mechanisms underlying the higher ischemic stroke incidence in white patients and intracerebral hemorrhage in Chinese patients.
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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 | 0.001 |
| 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".