Mechanical Thrombectomy Access Score: A Systematic Review and Modified Delphi of Global Barriers to Endovascular Therapy
Bibliographic record
Abstract
BACKGROUND: The availability of mechanical thrombectomy (MT) for acute ischemic stroke is limited, and vast disparities exist between countries. We aim to create a MT access score to measure the drivers of access to help quantify and accelerate treatment worldwide. METHODS: We used a systematic review complemented by a modified Delphi method. In the first of 3 rounds, 4 independent investigators performed a systematic literature review using key search terms that drive MT access, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. In the second round, a panel of 6 anonymous international experts selected key attributes needed for scoring. In the final round, a total of 12 attributes were selected on consensus, each given a score on a 0 to 3 scale. An ultimate MT access score (range, 0-36) was proposed as a new tool to use in identifying barriers to MT access and assist in providing an initial framework for public health interventions. RESULTS: Of 2864 abstracts screened, 121 studies were included in the final systematic review. A total of 34 attributes that potentially drive MT access were initially identified. In the final round, 12 attributes were selected by the expert panel: public awareness, emergency medical services transportation, prehospital large vessel occlusion screening, interhospital transfer policy, emergency department protocols, stroke imaging protocols, emergency department stroke expertise or telestroke availability, interventionalists, MT-capable centers, device availability, and insurance coverage. These attributes were weighted as part of the final score of 0 to 36. CONCLUSIONS: The MT access score represents the first tool to quantify barriers to global MT access. Its implementation stands not just as an academic achievement but as a beacon of hope for improving stroke care and outcomes worldwide, bringing us a step closer to bridging the gap in stroke treatment disparities.
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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 | 0.090 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.025 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".