Endovascular treatment of distal medium vessel occlusions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Endovascular treatment (EVT) has dramatically improved outcomes of patients suffering from acute ischemic stroke due to large vessel occlusion (LVO), becoming the standard of care. However, up to one-third of ischemic strokes are caused by distal medium vessel occlusions (DMVO), which are beyond the LVO territory. Medical management, including intravenous thrombolysis, leaves more than half of DMVO patients disabled at 3 months, with mortality exceeding 10%. In face of this grim prognosis, expanding EVT to DMVO has gained considerable interest. This review summarizes the clinical, anatomical, and imaging features of DMVO stroke, discusses recent EVT trial results and their interpretation, and outlines future directions for establishing safe and effective reperfusion strategies in this population. RECENT FINDINGS: Recent randomized trials investigating EVT for DMVO stroke yielded neutral results overall. However, they provided important insights about patient subgroups likely to benefit from intervention and set key challenges to improving the management of patients with DMVO. SUMMARY: While current evidence does not support routine EVT for DMVO stroke, the field is evolving rapidly. Ongoing advances in device technology, patient selection, and trial design hold promise for refining treatment and improving outcomes in carefully selected patients.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".