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Record W4220801997 · doi:10.1177/15910199221081243

Stentrievers : An engineering review

2022· review· en· W4220801997 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInterventional Neuroradiology · 2022
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineStroke (engine)Endovascular treatmentStentOcclusionMedical physicsKey (lock)Intensive care medicineSurgeryComputer scienceEngineeringMechanical engineeringAneurysm

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.086
GPT teacher head0.369
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it