You Cannot Manage What You Do Not Measure: Advances in Global Stroke Interventions and the Role of the Mechanical Thrombectomy Access Score
Bibliographic record
Abstract
Global disparities in stroke care, particularly in acute interventions like mechanical thrombectomy (MT), remain profound, with the Mechanical Thombectomy Global Access for Stroke study reporting a median global MT access of just 2.79%. Furthermore, the low- and middle-income countries (LMICs) have been recognized to be disproportionately burdened in this regard as compared with high-income countries. These observed inequities in stroke care impact not only clinical outcomes but also economic productivity and social systems. Recent advancements, such as TeleStroke networks, Mobile Stroke Units, and artificial intelligence-powered tools, have the potential to bridge these gaps. The Mechanical Thrombectomy Access Score (MTAS) offers a novel standardized approach to quantifying barriers to MT access and guiding targeted interventions to mitigate such obstacles. This review explores how MTAS enables the integration of these advancements into global stroke care systems, addressing inequities and optimizing outcomes. Emphasizing the importance of measuring access to manage inequities, we propose strategies to refine and validate MTAS while advocating for systemic investments to enhance global stroke care.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".