Prehospital stroke care in low- and middle-income countries: A World Stroke Organization (WSO) scientific statement
Bibliographic record
Abstract
Evidence-based prehospital stroke care is effective in reducing stroke-related mortality and morbidity. The crucial period from symptom awareness to presentation at the hospital, the first step in the World Stroke Organization Road Map to Quality Care, is under-resourced in the majority of low- and middle-income countries (LMICs). Key challenges focus on a lack of stroke action awareness as well as human resources trained in stroke care. We aimed to identify prehospital stroke practices in LMICs and identify where innovation may address service gaps. We conducted scoping reviews focused on key domains of prehospital stroke care in LMICs that include organization of services, stroke action awareness in the community, educating primary care physicians and traditional/faith healers, diagnostic tools for prehospital stroke detection, and emergency medical service (EMS) provision. We sought to determine current practices and gaps in LMICs and evidence on effective interventions to address gaps in each domain. Recommendations are provided identifying priority considerations in each domain, based on evidence, and where lacking, expert opinion. Key recommendations include the need for adequately funded national-level strategies for prehospital stroke care; stroke action awareness education for the public, primary care physicians, community health workers, EMSs, and traditional and faith healers; affordable imaging solutions; and approaches to create or improve prehospital EMS (e.g., protocols). We found that efforts, although few, have been made to address gaps in LMICs; however, they have rarely been evaluated, and it is unclear if they are sustained. The required elements necessary to improve prehospital services and stroke outcomes are known. Creativity is and perseverance are required for implementation to ensure sustainability. This scientific statement has been reviewed and approved by the World Stroke Organization Executive.
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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 | 0.100 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".