Practical use and underlying physics of the BENCHMARK™ BMX™ 96 for large-bore aspiration thrombectomy: Case report of initial institutional experience
Bibliographic record
Abstract
Endovascular thrombectomy (EVT) is part of first-line intervention for acute ischemic stroke management. Recent technological advances have demonstrated that large-bore catheters are an attractive approach for EVT. A multitude of approaches such as A Direct Aspiration first Pass Technique (ADAPT) or in conjunction with stent retrieval (Solumbra technique) have been developed with increasingly large-bore catheters, demonstrating safety and efficacy. Furthermore, these techniques have demonstrated promise for the intervention of cerebral venous thrombosis as well as posterior circulation ischemic events. Recently, advances in neurointerventional catheters have focused on improved maneuverability to navigate the neurovasculature, as well as larger inner diameters for improved procedural versatility, including aspiration. We describe a case report highlighting our early institutional experience with the recently developed large-bore catheter, the BENCHMARK™ BMX™ 96. The case report entails near complete occlusion of the internal carotid artery from acute thrombus and the utility of the BMX™ 96 catheter for treatment of such extensive clot burden. The applicability of large-bore aspiration catheters, with an emphasis on recent advances, for mechanical thrombectomy in arterial as well as venous systems is discussed. To our knowledge, this is the first reported case of use of the BENCHMARK™ BMX™ 96 access system for EVT in acute ischemic stroke. Such new-generation large-bore catheters are a promising advance in neurointervention, and our early institution experience highlights the ease of use and versatility for neurointerventional procedures such as EVT.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".