Strategies to Improve Stroke Care Services in Low- and Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The burden of stroke in low- and middle-income countries (LMICs) is large and increasing, challenging the already stretched health-care services. AIMS AND OBJECTIVES: To determine the quality of existing stroke-care services in LMICs and to highlight indigenous, inexpensive, evidence-based implementable strategies being used in stroke-care. METHODS: A detailed literature search was undertaken using PubMed and Google scholar from January 1966 to October 2015 using a range of search terms. Of 921 publications, 373 papers were shortlisted and 31 articles on existing stroke-services were included. RESULTS: We identified efficient models of ambulance transport and pre-notification. Stroke Units (SU) are available in some countries, but are relatively sparse and mostly provided by the private sector. Very few patients were thrombolysed; this could be increased with telemedicine and governmental subsidies. Adherence to secondary preventive drugs is affected by limited availability and affordability, emphasizing the importance of primary prevention. Training of paramedics, care-givers and nurses in post-stroke care is feasible. CONCLUSION: In this systematic review, we found several reports on evidence-based implementable stroke services in LMICs. Some strategies are economic, feasible and reproducible but remain untested. Data on their outcomes and sustainability is limited. Further research on implementation of locally and regionally adapted stroke-services and cost-effective secondary prevention programs should be a priority.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".