Proportion of Different Subtypes of Stroke in China
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The goal of this article is to clarify the proportion of stroke subtypes in China, where stoke is the most common cause of death. METHODS: A total of 16,031 first-ever strokes in subjects >or=25 years of age were identified in 1991 to 2000 from 17 Chinese populations through a community-based cardiovascular disease surveillance program in the China Multicenter Collaborative Study of Cardiovascular Epidemiology. World Health Organization diagnosis criteria were used for classification of stroke subtypes. RESULTS: CT scan rate of stroke cases reached a satisfactorily high level only after 1996 in the study populations. In 8268 first-ever stroke events from 10 populations with CT scan rate >75% in 1996 to 2000, 1.8% were subarachnoid hemorrhage, 27.5% were intracerebral hemorrhage, 62.4% were cerebral infarction, and 8.3% were undetermined stroke. The proportion of intracerebral hemorrhage varied from 17.1% to 39.4% and that for cerebral infarction varied from 45.5% to 75.9% from population to population. The ratio of ischemic to hemorrhagic stroke ranged from 1.1 to 3.9 and averaged 2.0). The 28-day fatality rate was 33.3% for subarachnoid hemorrhage, 49.4% for intracerebral hemorrhage, 16.9% for cerebral infarction, and 64.6% for undetermined stroke. CONCLUSIONS: In our study, ischemic stroke was more frequent and its proportion was higher than hemorrhagic stroke in Chinese populations. Although hemorrhagic stroke was more frequent in Chinese than in Western populations, the variation in the proportion of stroke subtypes among Chinese populations could be as large as or larger than that between Chinese and Western populations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".