Abstract 147: Telestroke Network Mapping: An Update of the World Landscape for Remote Stroke Care
Bibliographic record
Abstract
Background: Telestroke has proven efficacy in improving clinical outcomes by providing rapid access to specialized stroke care, particularly in remote areas. However, the global implementation of telestroke networks remains uneven, with limited data on their structure and coverage outside of high-income countries (HICs). Objective: This study aimed to provide a comprehensive overview of the global landscape of telestroke networks, highlighting disparities and underscoring the need for universal guidelines. We aimed to map the global telestroke landscape, characterize existing networks, and identify disparities in access, technological adoption, and quality monitoring practices across different regions. Methods: We conducted a three-tiered identification process involving engagement with national stroke experts, stroke societies, and international authorities, supplemented by extensive literature and internet searches to identify providers involved in telestroke networks worldwide. A detailed 39-question survey was distributed to the leaders of identified telestroke networks, assessing their structural characteristics, operational processes, and quality monitoring practices. Results: A total of 254 telestroke networks were identified across 67 countries (Figure 1), with 69% located in HICs. The response rate to our survey was 34%, with 88 networks from 31 countries providing detailed data. Our findings reveal significant disparities in the establishment and operation of telestroke networks. HICs predominantly host large, well-established networks, with robust technological infrastructures and comprehensive quality monitoring. In contrast, networks in low- and middle-income countries (LMICs) are fewer, smaller, and often lack advanced technology and standardized quality assurance measures. Notably, 87% of networks established within the last three years are located in non-HIC regions, signaling a shift toward broader global implementation. Conclusion: This study provides one of the most comprehensive global mappings of telestroke networks to date, uncovering significant disparities in access, resources availability (Figure 2) and quality monitoring practices. While telestroke networks are expanding into LMICs, there remains a critical need for universally applicable guidelines that can be adapted to diverse resource settings.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".