Incidence and associated factors of pre-hospital care-seeking delay in people with acute ischemic stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite decades of educational efforts, patients with acute ischemic stroke (AIS) remain delayed in seeking medical care, which becomes the greatest obstacle to the successful management of the condition. OBJECTIVE: The objective of this study was to systematically explore the incidence and influencing factors of prehospital care-seeking delay in AIS patients. METHODS: We systematically searched the PubMed, Embase, Cochrane Library, Web of Science, and Cumulative Index to Nursing and Allied Health Literature from database inception to September 30, 2023. Meta-analysis was conducted using the Stata 15.0 software package. The pooled incidence was calculated using a random-effects model. The quality of studies reporting incidence data was assessed using Joanna Briggs Institute's Critical Appraisal Checklist and Newcastle-Ottawa Scale. Subgroup analyses were performed according to study location, country income, recruitment date, and age. RESULTS: Finally, 30 related articles were included, involving a total of 287,102 people. The estimated incidence of prehospital care-seeking delay was 68%, and there were differences in this incidence in different countries (p = 0.035). Meta-analysis results showed that the delay rate was highest in low-income countries (85%) and lowest in high-income countries (62%). Patients who live farther from hospitals, have a lower level of education, diabetes, hyperlipidemia, or a history of stroke are more likely to experience delays (all p < 0.05). Conversely, those who can recognize stroke symptom, perceive the severity of early symptom, understand thrombolysis treatment, atrial fibrillation, consciousness disturbance, visual disturbance, and symptom score at admission, emergency medical service use, and immediate help-seeking have a lower risk of delay (all p < 0.05). CONCLUSION: Prehospital care-seeking delays are common among patients with AIS, especially in low-income countries. To reduce delays, it is crucial to increase public awareness of stroke symptoms, improve education levels, and optimize healthcare accessibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".