A scoping review of the utilization of mobile stroke units in low and lower middle-income countries: current evidence, implications and future direction
Bibliographic record
Abstract
BACKGROUND: Low and Lower-Middle-Income Countries (LMICs) have the highest stroke incidence, prevalence, and case fatality rates globally. Current evidence suggests Mobile Stroke Units (MSUs) outperform traditional Emergency Medicine Services (EMS) in time metrics, cost-effectiveness, and long-term outcomes. MSUs could potentially improve stroke outcomes in resource-constrained settings by addressing critical challenges related to prehospital delays, health-seeking behavior, and access to expertise. PURPOSE: This scoping review aims to assess the existing literature and knowledge gaps on the utilization of mobile stroke units in LMICS, their impact on stroke outcomes, and cost-effectiveness. MATERIALS AND METHODS: We conducted a detailed search of PubMed, Scopus, CINAHL, African Index Medicus, and Publicly Available Content Database (ProQuest) inception to April 15, 2024. Google Scholar and TRIP Pro were also searched to identify Grey literature. African Journals Online, references were also hand-searched. RESULTS: Seven hundred and eighty-five studies were screened; only two met the eligibility criteria. Cherian et al. report the first use of a mobile stroke unit (MSU) in India, detailing its operations during the first year and the challenges encountered. According to the authors, fewer patients utilize MSUs in India compared to other parts of the world due to challenges such as a lack of awareness and affordability. Osuegbu et al. also report the absence of both fixed and mobile stroke units in Rivers State, Nigeria. CONCLUSION: There is currently very limited data to support the contextual suitability of MSU or implementation strategies to guide its integration into stroke care systems in LMICs. Further research is needed to examine the utilization, barriers, impact, and cost-effectiveness of Mobile Stroke Units (MSUs) in low- and middle-income countries. This could inform stakeholders and policymakers about the potential role and value of MSUs within stroke care systems in these 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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.031 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".