China Stroke Statistics 2019: a wealth of opportunities for stroke prevention
Bibliographic record
Abstract
The China Stroke Statistics published in this issue of the journal1 represent an enormous undertaking. It included data on 3 010 204 patients who had a stroke admitted in 2018 to 1853 tertiary care hospitals and compiled data from multiple sources on stroke and stroke risk factors in China. The authors and the huge team of workers who must have compiled the statistics are to be commended on this enormous effort. In the report is a wealth of data on what is wrong in China to cause such a high risk of stroke but also a wealth of opportunity to make a difference. The reason this is so important is that ~80% of strokes are preventable. According to the 2016 Global Burden of Disease Study, China had the highest estimated lifetime risk of stroke from age 25 years onwards of up to 39.3%, compared with 22.2% in Western Europe and 22.4% in high-income North America.2 It is evident from the ratio of strokes to myocardial infarctions in China that hypertension is a major driver of stroke risk. In North America, myocardial infarctions (MI) outnumber strokes, but in China, stroke was historically much more common, though myocardial infarctions have been increasing in recent years. In 2003, in urban China, deaths from stroke were 8.5 times as common as deaths from MI; by 2013, strokes had increased by 26.6%, whereas death from MI increased by 213%, and deaths from stroke were only 2.5 times that of death from MI. This change is no doubt due to increased intake …
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".