Endovascular Treatment of Medium Vessel Occlusion Stroke
Bibliographic record
Abstract
Approximately one-third of acute ischemic strokes with an identifiable vessel occlusion are caused by medium vessel occlusion (MeVO), that is, nonlarge vessel occlusions that are potentially amenable to endovascular treatment (EVT). Management of patients with MeVO is challenging in many ways: detecting MeVOs can be challenging, particularly for inexperienced physicians, and in busy clinical routine, MeVOs, therefore, remain sometimes undiagnosed. While the clinical course of MeVO stroke with medical management, including intravenous thrombolysis, is by no means, benign, it is more favorable compared with large vessel occlusion. At the same time, EVT complication rates are higher, and thus, the marginal benefit of EVT beyond best medical management is expected to be smaller and more challenging to detect if it were present. Several randomized controlled trials are currently underway to investigate whether and to what degree patients with MeVO may benefit from EVT and will soon provide robust data for evidence-based MeVO EVT decision-making. In this review, we discuss different ways of defining MeVOs, strategies to optimize MeVO detection on imaging, and considerations for EVT decision-making in the setting of MeVO stroke. We discuss the technical challenges related to MeVO EVT and conclude with an overview of currently ongoing MeVO EVT trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 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".