Age and geographic disparities in acute ischaemic stroke prehospital delays in China: a cross-sectional study using national stroke registry data
Bibliographic record
Abstract
Background: Stroke has been the leading cause of death in China for decades. The extremely low intravenous thrombolysis rate is largely due to the prehospital delays that make patients ineligible for the time-sensitive therapy. Limited studies evaluated prehospital delays across China. We investigated prehospital delays in the stroke population across China and the associated age, rurality, and geographic disparities. Methods: A cross-sectional study design was employed using the Bigdata Observatory platform for Stroke of China in 2020, the nationwide, prospective, multicentre registry of patients with acute ischaemic stroke (AIS). Mixed-effect regression models were used to account for the clustered data. Findings: The sample contained 78,389 AIS patients. The median onset-to-door (OTD) time was 24 h, with only 11.79% (95% confidence interval [CI]: 11.56-12.02%) patients arriving at hospitals within 3 h. About 12.43% (95% CI: 12.11-12.74%) of patients 65 years or older arrived at hospitals within 3 h, which was significantly higher than the young and middle-aged patients (11.03%; 95% CI: 10.71-11.36%). After controlling for potential confounders, young and middle-aged patients were less likely to present to hospitals within 3 h (adjusted odds ratio: 0.95; 95% CI: 0.90-0.99) compared to patients 65 years or older. The 3-h hospital arrival rate was the highest in Beijing (18.40%, 95% CI: 16.01-20.79%), which was almost 5 times higher than that in Gansu (3.45%, 95% CI: 2.69-4.20%). The arrival rate in urban areas was almost 2 times higher than that in rural areas (13.35% versus. 7.66%). Interpretation: We found that the low rates of timely arrival at hospitals after a stroke is more salient in the younger population, rural settings, or those residing in less developed geographic regions. This study calls for more tailored interventions focusing on younger people, rural areas, and less developed geographic regions. Funding: The National Natural Science Foundation of China; CIHR, Grant/Award Number: 81973157, PI: JZ. Natural Science Foundation of Shanghai; CIHR, Grant/Award Number: 17dz2308400, PI: JZ. Funding from the University of Pennsylvania; Grant/Award Number: CREF-030, PI: RL.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".