Disparity in risk factors of ischemic stroke in four coastal-area hospitals in China
Bibliographic record
Abstract
Background: Currently, ischemic stroke is the leading cause of death in China. To compare regional differences of ischemic stroke, we analyzed the clinical characteristics of patients with ischemic stroke in four regionally representative hospitals in China. Methods: We conducted a retrospective study at four tertiary hospitals in east China, with regionally representative patients. The associated factors include hypertension, diabetes mellitus, coronary heart disease, hyperlipidemia and a combination of these factors. The standardized ratio (SR), estimated as the observed number divided by the expected number, computed as the sum of predicted probabilities from a multivariable logistic regression model derived using data from all other cities, was used to compare to average levels. Results: A total of 34,707 patients were included. The number of patients increased with age in all four hospitals and patients were predominantly male. The number of ischemic stroke cases with related factors increased with age, except for hyperlipidemia. There was no significant gender difference when multiple related factors existed simultaneously. Coronary heart disease had a more significant impact on ischemic stroke in Qingdao Municipal Hospital and the First Hospital of Qinhuangdao, while hyperlipidemia had a significant influence on ischemic stroke in the First Hospital of Qinhuangdao. Conclusions: At four hospitals in east China, with the increase of age, the risk factors associated with ischemic stroke increased, and the distribution of ischemic stroke-related factors showed regional differences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".