Endovascular Device Choice and Tools for Recanalization of Medium Vessel Occlusions: Insights From the MeVO FRONTIERS International Survey
Bibliographic record
Abstract
Background: Endovascular treatment (EVT) for stroke due to medium vessel occlusion (MeVO) can be technically challenging. Devices and tools are rapidly evolving. We aimed to gain insight into preferences and global perspectives on the usage of endovascular tools in treating MeVOs. Methods: We conducted an international survey with seven scenarios of patients presenting A3, M2/3, M3, M3/4, or P2/3 occlusions. Respondents were asked for their preferred first-line endovascular approach, and whether they felt that the appropriate endovascular tools were available to them. Answers were analyzed by occlusion location and geographical region of practice, using multinomial/binary logistic regression. Results: A total of 263 neurointerventionists provided 1836 responses. The first-line preferences of physicians were evenly distributed among stent-retrievers, combined approaches, and aspiration only (33.2, 29.8, and 26.8%, respectively). A3 occlusions were more often treated with stent-retrievers (RR 1.21, 95% CI: 1.07–1.36), while intra-arterial thrombolysis was more often preferred in M3 (RR 2.47, 95% CI: 1.53–3.98) and M3/4 occlusions (RR 7.71, 95% CI: 4.16–14.28) compared to M2/3 occlusions. Respondents who thought appropriate tools are currently not available more often chose stent retrievers alone (RR 2.07; 95% CI: 1.01–4.24) or intra-arterial thrombolysis (RR 3.35, 95% CI: 1.26–8.42). Physicians who stated that they do not have access to optimal tools opted more often not to treat at all (RR 3.41, 95% CI: 1.11–10.49). Stent-retrievers alone were chosen more often and contact aspiration alone less often as a first-line approach in Europe (RR 2.12, 95% CI: 1.38–3.24; and RR 0.49, 95% CI 0.34–0.70, respectively) compared to the United States and Canada. Conclusions: In EVT for MeVO strokes, neurointerventionalists choose a targeted vessel specific first-line approach depending on the occlusion location, region of practice, and availability of the appropriate tools.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".