Comparisons of Risk Factors for Intracerebral Hemorrhage versus Ischemic Stroke in Chinese Patients
Bibliographic record
Abstract
BACKGROUND: Chinese populations have a higher stroke incidence, a higher proportion of intracerebral hemorrhage (ICH), and a lower proportion of ischemic stroke (IS) as compared with white populations. The reasons are not fully understood. METHODS: To evaluate the differences of major risk factors between ICH and IS in Chinese stroke patients, we analysed acute ICH and IS patients consecutively recruited in National Taiwan University Hospital Stroke Registry from 2006 to 2011. We used multiple logistic regression models to examine the associations of risk factors with ICH vs. IS. Also, we conducted subgroup analyses when a strongly significant interaction was detected. RESULTS: We included a total of 1,373 ICH and 4,953 IS patients. ICH patients were younger than IS patients (mean age 61 vs. 68 years, p < 0.001), but there was no significant difference in gender (males 62 vs. 59%, p = 0.064). A logistic regression model adjusted for age, gender, and other major risk factors showed that both hypertension (OR 2.23, 95% CI 1.74-2.87) and alcohol intake (OR 1.44, 95% CI 1.16-1.77) had significantly stronger associations with ICH than IS, whereas diabetes, atrial fibrillation, ischemic heart disease, hyperlipidemia, smoking, and transient ischemic attack were less associated with ICH than IS. In subgroup analyses, the association of hypertension with ICH vs. IS was more marked in younger patients. CONCLUSION: Hypertension and alcohol intake are more strongly associated with ICH than IS in Chinese stroke patients, especially in younger patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".