Nationwide analysis of routine clinical practices in the management of acute ischemic stroke patients in China
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Despite stroke center advancements in China, real-world adherence to acute care protocols of ischemic stroke remains understudied. We aimed to systematically investigate the clinical characteristics and in-hospital treatment of acute ischemic stroke (AIS) patients, and explore their association with prognosis. METHODS: We developed a nationwide cohort of AIS using data from the China National Electronic Disease Surveillance System. Patients were identified from the first discharge diagnosis. Comorbidities and prescription names were standardized by natural language processing and manual verification. Stepwise Cox regression models with fixed and time-dependent covariates explored the possible association between treatments and in-hospital mortality. RESULTS: This cohort included 14,046 patients with AIS from 111 hospitals between 2015 and 2020. Only a small proportion of patients received intravenous thrombolysis (2.76%) or endovascular interventional therapy (3.23%). Neuroprotective agents were used by 59.90% of patients, and dual antiplatelet therapy by 45.77%. Most patients (80.79%) received traditional Chinese medicine, including Chinese patent medicines (79.04%), Chinese herbal medicine slices (10.95%), and acupuncture (7.35%). Rehabilitation services were provided to 7.48% of patients. Cox regression analysis showed that neuroprotective agents (hazard ratio (HR) = 0.73, 95% confidence interval (CI) = 0.61-0.88), Chinese patent medicine (circulate blood and transform stasis: 0.49, 0.41-0.59; clear heat and remove toxins: 0.71, 0.52-0.98), Chinese herbal medicine slices (0.28, 0.17-0.44), acupuncture (0.58, 0.41-0.84), and rehabilitation therapies (0.95, 0.93-0.97) were potentially associated with reduced in-hospital mortality risk. CONCLUSIONS: Our findings showed relatively low utilization rates of thrombolytic (2.76%) and interventional therapies (3.23%) in China, highlighting the urgent need to improve access to these evidence-based reperfusion strategies. The use of neuroprotective agents, Chinese herbal medicine, acupuncture, and rehabilitation might be associated with reduced in-hospital mortality in AIS patients; however, future high-quality prospective studies are still warranted to confirm the clinical effects of these treatments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".