Computer Modeling of Clot Retrieval—Circle of Willis
Bibliographic record
Abstract
Endovascular clot retrieval, often referred to as mechanical thrombectomy, has transformed the treatment of patients with ischemic stroke based on an underlying large cerebral vessel occlusion, ranging from the extracranial internal carotid artery (ICA) to the M1 (proximal) segment of the middle cerebral artery (MCA). The aim of this study was to evaluate the effect of a progressive occlusion of the extracranial portion of the ICA on the cerebral blood flow either with a conventional guiding catheter or a balloon-guiding catheter, which enables the operator to completely occlude the parent artery by inflating the balloon around the tip of this type of guiding catheter. We evaluated the impact of flow reduction in the ICA in the setting of ipsilateral MCA occlusion given the different configurations of the circle of Willis (CoW). The computer model of cerebral arteries was based on anatomical works by Rhoton (1) and van der Eecken (2). The interactive experimental results are available on the web at https://gntem3.shinyapps.io/ecrsim. In the setting of left MCA occlusion, compensation from the anterior and posterior communicating artery preserved the flow in the left anterior cerebral artery (ACA) but not the left MCA branches. Under selected CoW configurations, such as classic, missing Acom, or missing A1 segment of the ACA and concurrent right ICA occlusion, there was a progressive decrease of flow in the left ACA to a minimum of 78% when the simulated catheter fully occluded the left ICA. Flow collapsed (<10%) in the left ACA and MCA branches under CoW configurations, such as bilateral fetal PCA. In summary, compensatory flow collapsed under certain clot retrieval scenarios and unusual configurations of CoW.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| 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".