Current management of patients with ST elevation myocardial infarction in Metropolitan Beijing, China
Bibliographic record
Abstract
Purpose To assess clinical practices and in-hospital outcomes of patients with ST elevation myocardial infarction (STEMI) in Beijing, China. Methods This study was a prospective multicentre registry study in Metropolitan Beijing, China. Demographics, delay time, management strategy and in-hospital outcome data were collected from patients with STEMI enrolled in 19 hospitals. Results A total of 803 patients (mean age 61±13 yr ,22% women and 42.1% ? 65 yr) with STEMI were enrolled. More than half had a history of hypertension (50.4%) and current smoking (55.2%). Six hundred and fifty patients (80.9%) received reperfusion trerapy: 124 (15.4% ) treated with thrombolysis and 526 (65.5% ) with primary percutaneous coronary intervention (PCI). The median door-to-needle time for thrombolysis was 83 min and door-to-balloon time for primary PCI was 132 min. Only 7% of patients treated with thrombolysis met the guidelines goal of the door-to-needle time ? 30min and 22% of patients had PCI performed in ? 90 min. Aspirin was prescribed in 97.8% of patients, low molecular weigh heparin in 92%, statins in 91.0%, ?-blockers in 76.7%, ACE inhibitors in 73.5%, clopidogrel in 89.7% and GP IIb/IIIa antagonists in 19.3%. In-hospital mortality was 5.4%. Conclusion Recommended Clinical Guidelines treatments are largely implemented in patients with STEMI in Beijing. However, many patients were not reperfused within the recommended times. There remains important potential for improvement in the administration of reperfusion therapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".