HEADS UP 2024: Policy Efforts to Improve Equitable Access to Acute Stroke Care Globally
Bibliographic record
Abstract
Stroke is a leading cause of death and disability worldwide, particularly affecting low- and middle-income countries. Despite the benefit of stroke units and advancements in treatments such as intravenous thrombolysis and mechanical thrombectomy, achieving equitable access to these interventions remains challenging because of disparities in resources, infrastructure, and trained personnel. Additionally, the lack of awareness about stroke symptoms and the absence of organized pre-hospital care leads to delayed arrivals at hospitals equipped for treatment and postponed transfers to specialized centers. Moreover, insufficient secondary prevention strategies and rehabilitation services increase the chances of recurrent strokes and long-term disability. Despite the proven cost-effectiveness of treatment, the associated costs pose a substantial barrier to accessing timely care in low-resource countries. To reduce disparities and enhance global stroke care, confronting these challenges requires comprehensive strategies, including government policies, strong political commitment, and improved health care infrastructure using innovative financing. To address these issues, various initiatives aim to enhance accessibility to stroke care, including the World Stroke Organization Implementation Task Force, which includes support for implementing services, training programs for health care professionals, and telemedicine to extend stroke expertise to underserved areas. At the same time, the Certification of Stroke Centers Program promotes quality in care delivery, and the Global Stroke Alliance promotes international collaboration, joining stroke specialists and government representatives to establish national plans for stroke. This paper examines disparities and barriers to access, outlines current efforts to implement public policies in low- and middle-income countries, and proposes a step-by-step approach with concrete implementation strategies.
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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 | 0.035 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.059 | 0.014 |
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".