INTERNATIONAL PRACTICES IN OPTIMIZING TIME METRICS FOR REPERFUSION THERAPY IN ACUTE ISCHEMIC STROKE
Bibliographic record
Abstract
Introduction. Acute ischemic stroke (AIS) remains one of the leading causes of mortality and long-term disability worldwide. Reperfusion therapy, including thrombolysis and thrombectomy, is highly time-sensitive and significantly improves patient outcomes when administered promptly. However, international variations in treatment time metrics and accessibility pose challenges to optimizing outcomes globally. The aim of the review:This review aims to analyze key time metrics for reperfusion therapy, such as door-to-puncture (DTP) and door-to-groin puncture (DTGP), in high- and upper-middle-income countries. It seeks to identify factors influencing treatment efficiency and outcomes to provide actionable recommendations for healthcare systems, including Kazakhstan. Materials and methods:A systematic review was conducted using PubMed, Scopus, Web of Science, and Cochrane Library databases. Studies published between 2005 and 2021 were included, focusing on time metrics such as DTP, onset-to-needle (OTN), DTGP, onset-to-groin puncture (OTGP), and onset-to-door (OTD). A total of 27 studies were selected using PRISMA guidelines, and key findings were extracted for comparative analysis. Results:The analysis revealed significant international variability in time metrics. Countries like Switzerland and the USA/Canada demonstrated shorter DTP and DTGP times, correlating with higher percentages of favorable outcomes at 90 days. These metrics underscore the importance of efficient emergency services and hospital workflows. Additionally, disparities in access to advanced stroke care were identified, particularly in regions with fewer stroke centers and endovascular specialists. Conclusion: This review highlights the critical role of optimizing time metrics to improve patient outcomes in AIS. International best practices, such as dual-modality approaches combining thrombolysis and thrombectomy, can serve as models for improving stroke care efficiency. For Kazakhstan, the findings emphasize the need to reduce treatment delays by enhancing prehospital systems and developing regional stroke centers. Future efforts should focus on addressing global disparities and standardizing key metrics to facilitate comparisons and improvements in care.
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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 | 0.107 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".