Endovascular treatment of anterior cerebral artery occlusions
Bibliographic record
Abstract
There are limited data on endovascular treatment (EVT) for anterior cerebral artery (ACA) occlusions. This review focuses on aspects related to ACA EVT: ACA anatomy, clinical and imaging findings, prognosis of ACA stroke, and ACA thrombectomy techniques. The ACA anatomy, and the regions supplied by the ACA, are highly variable; frequent anatomical variants include azygos ACA, triplicated ACA and fenestrations of the anterior communicating artery. ACA occlusions can be classified based on occlusion location, their continuity with other vessel occlusions (isolated ACA occlusion vs ACA occlusion as part of a carotid T occlusion) and etiology (primary-spontaneous ACA occlusion, vs secondary-spontaneous or iatrogenic due to clot fragmentation/migration). Symptoms of ACA stroke differ in severity and nature due to large inter-individual variations in territorial ACA blood supply. Generally, ACA strokes are severely disabling, and the typical clinical hallmark is a motor deficit of the contralateral lower extremity. Advanced imaging (CT perfusion, multiphase CT angiography) increases the likelihood of the correct diagnosis of ACA stroke and should be obtained on routine basis.Available data for ACA EVT suggest its feasibility and safety while clinical outcomes are often unfavorable with conservative management. Therefore, the potential benefit of EVT seems obvious. An optimized endovascular approach for ACA thrombectomy comprises the development and use of smaller and softer devices that can be delivered through small microcatheters with an optimized vector of force. Ultimately, generating high-level evidence for ACA EVT from randomized trials remains warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".