Cardiovascular diseases in ChinaThis paper is one of a selection of papers in this Special Issue, entitled International Symposium on Recent Advances in Molecular, Clinical, and Social Medicine, and has undergone the Journal's usual peer-review process.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Statistics from the National Population Census of China revealed a significant increase in the Chinese population, from 590 million in 1953 to 1.26 billion in 2000. The average life expectancy increased to 71.4 years in 2000 compared with the expectancy of 68.6 years a decade before. World Health Organization statistics on the death rate for total cardiovascular disease, coronary heart disease, and stroke in men and women aged 35-74 years revealed discrepancies between rural and urban parts of China. The China Multicenter Collaborative Study of Cardiovascular Epidemiology indicated that cardiovascular disease was the major cause of death for both men and women, with stroke accounting for over 40% of deaths. Ischemia was shown to be the most common subtype of stroke in both sexes. Smoking was an independent risk factor for cardiovascular disease. The World Health Organization reported that the death rate attributable to tobacco was 6.0% worldwide and 9.2% in China in 1990. The latter is projected to reach 16.6% by 2020. In China, the prevalence of hypertension and diabetes mellitus, the two key risk factors of cardiovascular disease, have also increased significantly in the past 20 years. In addition, elevated blood pressure and plasma cholesterol were two important determinants of increased cardiovascular disease in eastern Asia. These studies indicate that an integrated management of comprehensive risk is urgently required to address China's increasing cardiovascular disease burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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 it