Evolution of Stroke Thrombectomy Techniques to Optimize First-Pass Complete Reperfusion
Bibliographic record
Abstract
Since 2015, endovascular therapy (EVT) has become the standard of care for acute ischemic stroke (AIS) due to large vessel occlusion. It is a safe and highly effective treatment, and its number needed to treat of 2.6 is one of the highest throughout medicine. The ultimate goal when performing EVT is to maximize chances of good outcome through achievement of fast first-pass complete reperfusion, as incomplete and delayed reperfusion increases complication rates and negatively affects outcome. Since EVT has been established as standard of care, new devices have been developed and treatment techniques have been refined. This review provides a brief overview about the rationale for and history of EVT, followed by a detailed step-by-step description of how to perform EVT using the BADDASS (BAlloon guide with large bore Distal access catheter with Dual Aspiration with Stent-retriever as Standard approach), a combined technique, which is in our opinion the safest and most effective way to achieve fast first-pass complete reperfusion. We also discuss treatment strategies for patients with simultaneous high-grade carotid stenosis/pseudoocclusion/occlusion and gaining carotid access in challenging arch anatomy, as these are commonly encountered situations in AIS, and conclude with an outlook on new technologies and future directions of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".