Surveillance of Trend and Distribution of Stroke Mortality by Subtype, Age, Gender, and Geographic Areas in Tianjin, China, 1999–2006
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to analyze the epidemiological trend and distribution of stroke mortality in the city of Tianjin, China, in order to provide evidence for the prevention and control of stroke. METHODS: The study was based on 102 718 cases of stroke mortality in Tianjin between 1999 and 2006. The cause of death was coded according to the International Classification of Diseases into stroke subtypes. Standardized mortality rates were calculated for stroke and its subtypes, adjusted for age and gender using the year 2000 world standard population. The age, gender, and geographic distribution of stroke and subtype mortality were analyzed. chi2-tests were used to determine the statistical significance of differences in mortality trends. RESULTS: The stroke mortality rate in Tianjin declined from 133.52/100 000/year in 1999 to 102.52/100 000/year in 2006. The stroke mortality rate for males was higher than that for females. Stroke mortality rates increased with increasing age. The subtypes of stroke have changed considerably in Tianjin. Hemorrhagic was major in 1999-2001, while cerebral infarction attained the first rank and accounted for more than 50% of stroke mortality in 2002-2006. The most pronounced finding was that the proportion of ischemic stroke was 66.65% in the urban population and over 20% higher than that in the rural area. Stroke in the suburban area was mainly hemorrhagic stroke, up to 62.67%. CONCLUSIONS: There are significant differences in the distribution of stroke mortality by subtype, age, gender, and geographic areas in Tianjin, China. Various subtypes of stroke are associated with different risk factors and therefore require different public health prevention and control measures. This study provides pertinent information for formulation of measures for the prevention and control of stroke.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".