Thrombectomy for Distal, Medium Vessel Occlusions
Bibliographic record
Abstract
Endovascular thrombectomy (EVT) is well established as a highly effective treatment for acute ischemic stroke (AIS) due to proximal, large vessel occlusions (PLVOs). With iterative further advances in catheter technology, distal, medium vessel occlusions (DMVOs) are now emerging as a promising next potential EVT frontier. This consensus statement integrates recent epidemiological, anatomic, clinical, imaging, and therapeutic research on DMVO-AIS and provides a framework for further studies. DMVOs cause 25% to 40% of AISs, arising as primary thromboemboli and as unintended consequences of EVT performed for PLVOs, including emboli to new territories (ENTs) and emboli to distal territories (EDTs) within the initially compromised arterial field. The 6 distal medium arterial arbors (anterior cerebral artery [ACA], M2–M4 middle cerebral artery [MCA], posterior cerebral artery [PCA], posterior inferior cerebellar artery [PICA], anterior inferior cerebellar artery [AICA], and superior cerebellar artery [SCA]) typically have 25 anatomic segments and give rise to 34 distinct arterial branches nourishing highly differentiated, largely superficial cerebral neuroanatomical regions. DMVOs produce clinical syndromes that are highly heterogenous but frequently disabling. While intravenous fibrinolytics are more effective for distal than proximal occlusions, they fail to recanalize one-half to two-thirds of DMVOs. Early clinical series using recently available, smaller, more navigable stent retriever and thromboaspiration devices suggest EVT for DMVOs is safe, technically efficacious, and potentially clinically beneficial. Collaborative investigations are desirable to enhance imaging recognition of DMVOs; advance device design and technical efficacy; conduct large registry studies using harmonized, common data elements; and complete formal randomized trials, improving treatment of this frequent mechanism of stroke.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".