Discrepancies between current and ideal endovascular stroke treatment practice in Europe and North America: Results from UNMASK EVT, a multidisciplinary survey
Bibliographic record
Abstract
BACKGROUND: Since 2015, endovascular therapy has been established as a standard of care for acute stroke. This has caused major challenges regarding the organization of systems of care, which have to meet the increasing demand for thrombectomies. This study aims to evaluate how endovascular therapy decisions made by European and North American physicians under their current local resources differ from those made under assumed ideal conditions. METHODS: In an international, multidisciplinary survey, physicians involved in acute stroke care were asked to give their treatment decisions to 10 out of 22 randomly assigned stroke case-scenarios. Participants stated (a) their treatment approach under assumed ideal conditions (without any external limitations) and (b) the treatment they would pursue under their current local resources. Resources gaps (ideal minus current endovascular therapy rates) were calculated for different countries/states/provinces and correlated to economic and healthcare key metrics (gross domestic product-per-capita, public or private health insurance coverage, etc.). RESULTS: A total of 607 physicians, among them 218 from North America and 136 from 25 European countries, responded to the survey. Resources gaps in the majority of North American states/provinces and European countries were small (<5%). The highest gaps were observed among few European countries, namely Poland (30%) and the United Kingdom (33%). The magnitude of the resources gap did not correlate to national economic or healthcare metrics. DISCUSSION AND CONCLUSION: In the majority of North American states/provinces and European countries covered in this study, the discrepancy between endovascular therapy decisions under current local resources and assumed ideal conditions seems to be small, even in countries with a limited economic status and healthcare infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".