Abstract TP192: Comparison of Stroke Subtype and Admission Rate between Beijing, China and Ontario, Canada - A Population-Based Approach
Bibliographic record
Abstract
Objective: Previous studies suggested Chinese were more likely to experience hemorrhagic stroke than Caucasian, however most of those studies were hospital-based and were performed more than a decade ago. We conducted a population-based study to investigate stroke subtypes and admissions rates in Beijing, China and Ontario, Canada from 2007 to 2009. Methods: We identified all admissions for stroke or transient ischemic attack (TIA) in Beijing from the Discharge Abstract Database maintained by Beijing Public Health Information Center, and in Ontario from the Discharge Abstract Database maintained by the Canadian Institute for Health Information, using ICD-10 codes I63 and I64 (ischemic stroke), I60 (subarachnoid hemorrhage) and I61 (intracerebral hemorrhage) and G45 (TIA). Patients with medical history of stroke, age under 20 years were excluded. If patient had multiple hospitalizations, only the first one was included. We adjusted the world population in 2007 as the standard to calculate the gender/age standardized admission rates. Results: During the study period, Beijing and Ontario had similar population size (~10 million) and age structure. In total, 140,574 patients in Beijing and 41,477 patients in Ontario had a first admission for stroke or TIA. Overall age/sex standardized admission rates were higher in Beijing than in Ontario (357 vs. 100 per 100,000). Of these patients, those in Beijing were younger than those in Ontario (Median of age in years: 66 vs. 76, p<0.001), and more likely to be male (58% vs. 49%, p<0.001), and to be TIA (22% vs. 17%, p<0.001), and less likely to have hypertension (68% vs. 75%, p<0.001) and diabetes (25% vs. 32%, p<0.001). Of these stroke patients in Beijing and Ontario, they were similar in subtype, ischemic stroke (84% vs. 82%) and hemorrhagic stroke (16% vs. 18%). The median length of hospital stay was much longer in Beijing than in Ontario (median 15 vs. 7 days, p<0.001). Conclusion: There were substantial differences in baseline patient characteristics, admission rates and length of stay, but similar stroke subtypes between Beijing and Ontario. These findings suggest that enhanced stroke primary prevention efforts are needed to reduce the burden of stroke in China.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".