Stentrievers : An engineering review
Bibliographic record
Abstract
The advent of endovascular therapy for acute large vessel occlusion has revolutionized stroke treatment. Timely access to endovascular therapy, and the ability to restore intracranial flow in a safe, efficient, and efficacious manner has been critical to the success of the thrombectomy procedure. The stentriever has been a mainstay of endovascular stroke therapy, and current guidelines recommend the usage of stentrievers in the treatment of large vessel occlusion stroke. Despite the success of existing stentrievers, there continues to be significant development in the field, with newer stentrievers attempting to improve on each of the three key aspects of the thrombectomy procedure. Here, we elucidate the technical requirements that a stentriever must fulfill. We then review the basic variables of stent design, including the raw material and its form, fabrication method, geometric configuration, and further additions. Lastly, a selection of stentrievers from successive generations are reviewed using these engineering parameters, and clinical data is presented. Further avenues of stentriever development and testing are also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".