Abstract WP372: International Comparison of Patient Characteristics and Quality of Care for Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose: Adherence to evidence-based guidelines is an important indicator of hospital stroke care quality; yet, there is lack of assessment of patient characteristics and performance measures in acute ischemic stroke for most world regions. Methods: We analyzed data of 19,604 acute ischemic stroke patients in China National Stroke Registry and 194,876 patients in GWTG-Stroke registry in US between 2012 to 2013 and compare their baseline characteristics and hospital performance measures. Results: Compared with US, Chinese patients were younger and had lower prevalence of comorbidities except for history of stroke/TIA or smoking. NIHSS was similar (China vs. US: median 4 [IQR 2-7] vs. 4 [1-10]). Chinese patients were more likely to have delays from last-known-well to hospital arrival (median 1,318 minutes [330-3,209] vs. 644 [142-2,055]), less likely to receive thrombolytic therapy (2.5% vs. 8.1%), and were more likely to experience treatment delays (door-to-needle time 95 minutes [72-112] vs. 62 [49-85]). Adherence to early and discharge antithrombotics, smoking cessation counseling, and dysphagia screening were relatively high (e.g.>80%) in both countries. However, large gaps existed in administration of intravenous thrombolytics within 3 hours (18.3% vs. 83.6%), door-to-needle time ≤60 minutes (14.6% vs. 48.0%), deep vein thrombosis prophylaxis (65.0% vs. 97.8%), anticoagulation for atrial fibrillation (21.0% vs. 94.4%), lipid treatment for low-density lipoprotein > 100 mg/dL (66.3% vs. 95.8%), and rehabilitation assessment (58.8% vs. 97.4%). Door-to-CT ≤25 minutes was relatively low in both countries (26.4% vs. 27.9%). Conclusions: We found significant differences in clinical characteristics and gaps in adherence for certain performance measures between China and US. Additional efforts are needed for continued improvements in acute stroke care and secondary prevention in both nations, especially 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".