Workflow patterns and potential for optimization in endovascular stroke treatment across the world: results from a multinational survey
Bibliographic record
Abstract
BACKGROUND: The benefit of endovascular treatment (EVT) is highly time-dependent, and treatment delays reduce patients' chances to achieve a good outcome. In this survey-based study, we aimed to evaluate current in-hospital EVT workflow characteristics across different countries and hospital settings, and to quantify the time-savings that could be achieved by optimizing particular workflow steps. METHODS: In a multinational survey, neurointerventionalists were asked to provide specific information about EVT workflows in their current working environment. Workflow characteristics were summarized using descriptive statistics and stratified by country and physician characteristics, such as age, career stage, personal and institutional caseload. RESULTS: Among 248 respondents from 48 countries, pre-notification of the neurointerventional team was used in 70% of cases. The emergency department (ED) and CT scanner, and the CT scanner and neuroangiography suite, were on different floors in 23% and 38%, respectively. Redundant procedures in the ED were often routinely performed, such as chest x-rays (in 6%). General anesthesia was the most frequently used anesthesia protocol for EVT (42%), and an anesthesiologist was available in 82% for this purpose. 52% of the participants used a pre-prepared EVT kit. CONCLUSION: The current structure of EVT workflows offers possibilities for improvement. While some bottlenecks, such as the spatial department set-up, cannot easily be resolved, pre-notification tools and pre-prepared EVT kits are more straightforward to implement and could help to reduce treatment delays, and thereby improve patient outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".